Calls for Service - Noise *This indicator has been discontinued

Calls for Service - Noise *This indicator has been discontinued Data and Resources Performance Indicator : CSPEC3CSV Calls for Service - Noise

The European Directive 2002/49/EC of 25 June 2002 on the assessment and management of environmental noise aims to assess the exposure to noise in the Member States in a harmonised manner. It defines them as data representations describing a sound situation according to a noise indicator, indicating exceedances of limit values, the number of people exposed. (Article 3 of the Decree of 24 March 2006 and Article 7 of the Decree of 4 April 2006). Noise cards have no prescriptive character. These are informational documents which are not enforceable at the legal level. As graphic elements, on the other hand, they can complement a local urban planning plan (PLU). As part of an urban travel plan (UDP), maps can be used to establish reference states and target areas where better traffic management is needed. To quantify the level of noise emitted by an infrastructure during an average day, two indices are used, the Lden index and the Ln index, recommended for all modes of transport at European level: — Lden: indicator representative of the average level over all 24 hours of the day, — LN: representative indicator of the average noise level for the period 22:00-6:00. (average night equivalent noise)

Noise levels are evaluated using numerical models (computer software) integrating the main parameters that influence noise and its propagation (traffic data, terrain topology, meteorological data,...). The noise maps thus produced are then cross-referenced with the demographic data of the areas concerned in order to estimate the population exposed to noise pollution. The sound level indicated on the noise maps is derived from a calculation method that gives approximate values and often higher than reality (maximalists) in a noise zone considered critical. An “in situ” noise control can precisely determine the noise to which a construction and its occupants may be exposed. The content and format of these maps meet the regulatory requirements of the European Directive 2002/49/EC on the management of noise in the environment.

Noise cards shall include, in accordance with the regulations:

• Sound level maps for a “reference situation” (so-called type a maps), showing equivalent noise-level curves in the territory. These are the layers Agregation_N_BRUIT_ZBR_R_A_LD_S_064.shp Agregation_N_BRUIT_ZBR_R_A_LN_S_064.shp

• Maps of areas affected by noise related to the noise classification of roadways in force (type b maps). Agregation_N_BRUIT_ZBR_R_B_00_S_064.shp

• Exceedance maps, representing areas likely to contain buildings with a modelled sound level above regulatory thresholds (type c maps). Agregation_N_BRUIT_ZBR_R_C_LD_S_064.shp Agregation_N_BRUIT_ZBR_R_C_LN_S_064.shp

(There is also the layer of noise sector B on motorways A63 and A64 SECTOR_BR_B_AUTOROUTE.shp)

The roads concerned were selected in accordance with the Prefectural Decree approving strategic noise maps of the Land Transport Infrastructures with an annual traffic of more than 3 million vehicles in the department of Pyrénées-Atlantiques. (Prefectural decree of 12 October 2018 n°64-2018-10-12-001).

This decree lists the main road infrastructure of the department of the Atlantic Pyrenees: — national motorways granted A63 and A64 — national N134 — departmental D2 D6 D9 D33 D37 D281 D309 D501 D635 D802 D810 D811 D817 D834 D911 D912 D918 D932 D936 D938 D943 D947 — several communal roads of the communes of Anglet, Bayonne, Biarritz, Billère, Bizanos, Gelos, Hendaye, Idron, Jurançon, Lescar, Lons, Oloron-Sainte-Marie, Pau, Saint-Jean-de-Luz

Increasing anthropogenic noise is having a global impact on wildlife, particularly due to the masking of crucial acoustical communication. However, there have been few studies examining the impacts of noise exposure on communication in free-ranging terrestrial mammals. We studied alarm calls of black-tailed prairie dogs (Cynomys ludovicianus) across an urban gradient to explore vocal adjustment relative to different levels of noise exposure. There was no change in the frequency 5%, peak frequency or duration of the alarm calls across the noise gradient. However, the minimum frequency – a commonly used, yet potentially compromised metric – did indeed show a positive relationship with noise exposure. We suspect this is a result of masking of observable call properties by noise, rather than behavioural adjustment. In addition, the proximity of conspecifics and the distance to the perceived threat (observer) did affect the frequency 5% of alarm calls. These results reveal that prairie dogs do not appear to be adjusting their alarm calls in noisy environments but likely do in relation to their social context and the proximity of a predatory threat. Anthropogenic noise can elicit a range of behavioural and physiological responses across taxa, but elucidating the specific mechanisms driving these responses can be challenging, particularly as these are not necessarily mutually exclusive. Our research sheds light on how prairie dogs appear to respond to noise as a source of increased risk, rather than as a distraction or through acoustical masking as shown in other commonly studied species (e.g. fish, songbirds, marine mammals).

Methods Prairie dog alarm calls were recorded across an urban gradient at three distinct colonies from 28 August to 6 December 2014. Alarm calls were elicited by the observer approaching a randomly selected prairie dog. Once the prairie dog began alarm calling the observer remained stationary and recorded 30 seconds of vocalization while the animal was in situ. A band-limited automated detector was used in Raven Pro v1.5 to select each of the individual barks in the 30-second calling bouts and to optimize extraction of call parameters. Before measurements were extracted on the individual barks, all detections were examined manually for accuracy and adjusted to maximize the detection of all barks within a recording period and to ensure the entire bandwidth and duration of calls were selected. Random selections of half of the barks in a calling bout (n = 4516) were then measured. Four acoustic metrics were calculated for each bark: (1) minimum frequency (Hz) – the lower frequency limit of the call, a commonly used metric in previous studies; (2) frequency 5% (Hz) – the frequency where the summed energy equals 5% of the total, a measure of lower frequency properties; (3) peak frequency (Hz) – the frequency with the highest concentration of energy; and (4) bark duration (milliseconds). Ambient sound levels were measured using a calibrated Larson-Davis 831 sound level meter (frequency weighting = A) over a 2-minute period as soon as the vocalization recording was completed. Sound pressure levels were measured as 1-second frequency weighted (12.5Hz - 20kHz) equivalent continuous levels (LAeq, 1s).

ContextProbing tasks are popular among NLP researchers to assess the richness of the encoded representations of linguistic information. Each probing task is a classification problem, and the model’s performance shall vary depending on the richness of the linguistic properties crammed into the representation.

This dataset contains five new probing datasets consist of noisy texts (Tweets) which can serve as a benchmark dataset for researchers to study the linguistic characteristics of unstructured and noisy texts.File StructureFormat: A tab-separated text file

Column 1: train/test/validation split (tr-train, te-test, va-validation)

     Column 2: class label (refer to the content

section for the class labels of each task file)

     Column 3: Tweet message (text)

     Column

4: a unique ID Contentsent_len.tsvIn this classification task, the goal is to predict the sentence length in 8 possible bins (0-7) based on their lengths; 0: (5-8), 1: (9-12), 2: (13-16), 3: (17-20), 4: (21-25), 5: (26-29), 6: (30-33), 7: (34-70). This task is called “SentLen” in the paper.word_content.tsvWe consider a 10-way classifications task with 10 words as targets considering the available manually annotated instances. The task is predicting which of the target words appears on the given sentence. We have considered only the words that appear in the BERT vocabulary as target words. We constructed the data by picking the first 10 lower-cased words occurring in the corpus vocabulary ordered by frequency and having a length of at least 4 characters (to remove noise). Each sentence contains a single target word, and the word occurs precisely once in the sentence. The task is referred to as “WC” in the paper. bigram_shift.tsvThe purpose of the Bigram Shift task is to test whether an encoder is sensitive to legal word orders. Two adjacent words in a Tweet are inverted, and the classification model performs a binary classification to identify inverted (I) and non-inverted/original (O) Tweets. The task is referred to as “BShift” in the paper. tree_depth.tsvThe Tree Depth task evaluates the encoded sentence's ability to understand the hierarchical structure by allowing the classification model to predict the depth of the longest path from the root to any leaf in the Tweet's parser tree. The task is referred to as “TreeDepth” in the paper. odd_man_out.tsv

The Tweets are modified by replacing a random noun or a verb o with another noun or verb r. The task of the classifier is to identify whether the sentence gets modified due to this change. Class label O refers to the unmodified sentences while C refers to modified sentences. The task is called “SOMO” in the paper.

WiserBrand's Comprehensive Customer Call Transcription Dataset: Tailored Insights

WiserBrand offers a customizable dataset comprising transcribed customer call records, meticulously tailored to your specific requirements. This extensive dataset includes:

User ID and Firm Name: Identify and categorize calls by unique user IDs and company names. Call Duration: Analyze engagement levels through call lengths. Geographical Information: Detailed data on city, state, and country for regional analysis. Call Timing: Track peak interaction times with precise timestamps. Call Reason and Group: Categorised reasons for calls, helping to identify common customer issues. Device and OS Types: Information on the devices and operating systems used for technical support analysis. Transcriptions: Full-text transcriptions of each call, enabling sentiment analysis, keyword extraction, and detailed interaction reviews.

Our dataset is designed for businesses aiming to enhance customer service strategies, develop targeted marketing campaigns, and improve product support systems. Gain actionable insights into customer needs and behavior patterns with this comprehensive collection, particularly useful for Consumer Data, Consumer Behavior Data, Consumer Sentiment Data, Consumer Review Data, AI Training Data, Textual Data, and Transcription Data applications.

WiserBrand's dataset is essential for companies looking to leverage Consumer Data and B2B Marketing Data to drive their strategic initiatives in the English-speaking markets of the USA, UK, and Australia. By accessing this rich dataset, businesses can uncover trends and insights critical for improving customer engagement and satisfaction.

Cases:

1. Training Speech Recognition (Speech-to-Text) and Speech Synthesis (Text-to-Speech) Models WiserBrand's Comprehensive Customer Call Transcription Dataset is an excellent resource for training and improving speech recognition models (Speech-to-Text, STT) and speech synthesis systems (Text-to-Speech, TTS). Here’s how this dataset can contribute to these tasks:

Enriching STT Models: The dataset includes a wide variety of real-world customer service calls with diverse accents, tones, and terminologies. This makes it highly valuable for training speech-to-text models to better recognize different dialects, regional speech patterns, and industry-specific jargon. It could help improve accuracy in transcribing conversations in customer service, sales, or technical support.

Contextualized Speech Recognition: Given the contextual information (e.g., reasons for calls, call categories, etc.), it can help models differentiate between various types of conversations (technical support vs. sales queries), which would improve the model’s ability to transcribe in a more contextually relevant manner.

Improving TTS Systems: The transcriptions, along with their associated metadata (such as call duration, timing, and call reason), can aid in training Text-to-Speech models that mimic natural conversation patterns, including pauses, tone variation, and proper intonation. This is especially beneficial for developing conversational agents that sound more natural and human-like in their responses.

Noise and Speech Quality Handling: Real-world customer service calls often contain background noise, overlapping speech, and interruptions, which are crucial elements for training speech models to handle real-life scenarios more effectively.

1. Training AI Agents for Replacing Customer Service Representatives WiserBrand’s dataset can be incredibly valuable for businesses looking to develop AI-powered customer support agents that can replace or augment human customer service representatives. Here’s how this dataset supports AI agent training:

Customer Interaction Simulation: The transcriptions provide a comprehensive view of real customer interactions, including common queries, complaints, and support requests. By training AI models on this data, businesses can equip their virtual agents with the ability to understand customer concerns, follow up on issues, and provide meaningful solutions, all while mimicking human-like conversational flow.

Sentiment Analysis and Emotional Intelligence: The full-text transcriptions, along with associated call metadata (e.g., reason for the call, call duration, and geographical data), allow for sentiment analysis, enabling AI agents to gauge the emotional tone of customers. This helps the agents respond appropriately, whether it’s providing reassurance during frustrating technical issues or offering solutions in a polite, empathetic manner. Such capabilities are essential for improving customer satisfaction in automated systems.

Customizable Dialogue Systems: The dataset allows for categorizing and identifying recurring call patterns and issues. This means AI agents can be trained to recognize the types of queries that come up frequently, allowing them to automate routine tasks such as ...

1. Human-caused noise pollution dominates the soundscape of modern ecosystems, from urban centers to national parks. Though wildlife can generally alter their communication to accommodate many types of natural noise (e.g. wind, wave action, heterospecific communication), noise pollution from anthropogenic sources pushes the limits of wildlife communication flexibility by causing loud, low-pitched, and near-continuous interference. Because responses to noise pollution are variable and taxa-specific, multi-species risk assessments and mitigation are not currently possible.
2. We conducted a meta-analysis to synthesize noise pollution effects on terrestrial wildlife communication. Specifically, we assessed: 1) the impacts of noise pollution on modulation of call rate, duration, amplitude, and frequency (including peak, minimum, and maximum frequency); and 2) the literature on anthropogenic noise pollution by region, taxa, study design, and disturbance type.
3. Terrestrial wildlife (results driven by avian studies) generally respond to noise pollution by calling with higher minimum frequencies, while they generally do not alter the amplitude, maximum frequency, peak frequency, duration, and rate of calling.
4. The literature on noise pollution research is biased towards birds, population-level studies, urban noise sources, and study systems in North America.

5. Policy applications Our study reveals the ways in which wildlife can alter their signals to contend with anthropogenic noise, and discusses the potential fitness and management consequences of these signal alterations. This information, combined with an identification of current research needs, will allow researchers and managers to better develop noise pollution risk assessment protocols and prioritize mitigation efforts to reduce anthropogenic noise.12-Mar-2021 Methods Literature Search Strategy and Inclusion Criteria

     We searched the peer-reviewed scientific literature to synthesize information regarding noise pollution impacts on wildlife acoustic communication and to assess research gaps and biases. We restricted the search to terrestrial systems because general approaches to noise pollution risk assessment and recommendations for noise mitigation already exist for some coastal and marine systems (Southall et al. 2007). Perhaps more importantly, a vast body of research conducted to date on marine wildlife has yielded valuable knowledge such as species-specific spectral sensitivity, critical impact thresholds, and mitigation effectiveness which can be drawn upon to advance general theory and research and to develop further regulatory guidelines (Erbe et al. 2016). Finally, the physics of sound transmission differ between water and air, affecting both how sound is perceived by organisms and potential mitigation strategies (Würsig et al. 2000, Shannon et al. 2015). We used Web of Science (search conducted 4/5/2018) to search for studies investigating the impact of noise pollution on wildlife modulation of call frequency, rate, duration, and amplitude (see Table 2 for specific search terms). We assessed these multiple communication response variables even though they may be related because each response may have different ecological and/or evolutionary implications. An initial search produced 815 studies. After implementing all inclusion criteria (see below), our search resulted in 181 data points from 32 studies representing six continents (Table 3).
   

We used the “Analyze Results” feature in Web of Science to filter out irrelevant disciplines (e.g., Audiology, Speech Pathology, nexcluded = 347). After compiling remaining results into a database, we removed duplicate studies (nexcluded = 5) and studies determined to be topically irrelevant based on reading of all titles (nexcluded = 117). We excluded studies broadcasting white noise as a treatment, as we were interested in responses to spectral characteristics that more closely match environmental noise pollution (i.e., loud, low-frequency sounds, nexcluded = 3). However, we retained one study that explicitly manipulated the characteristics of white noise to approximate low-frequency traffic sounds. We excluded studies conducted in a laboratory setting, as we were only interested in responses of free-living wildlife to noises experienced in their natural habitat (nexcluded = 5). After detailed screening of article texts, we removed studies that did not assess effects of noise pollution on the above focal response variables and studies with analysis methods or reporting that precluded us from extracting a relevant effect size (nexcluded = 59).

    For remaining studies, we extracted the location, focal taxa, response variable, sound source, and study design. We also extracted means, sample sizes, and standard deviations of response variables for studies assessing categorical predictor variables (e.g., call characteristics at quiet and noisy sites), or values of Pearson’s r for studies assessing continuous predictor variables (e.g., response characteristics over a gradient of decibel levels). In studies with multiple treatments, we used the two extreme ends of the environmental sound spectrum for analysis. For example, if a study tested call rates in “quiet”, “moderate”, and “loud” environments, we compared responses between “quiet” and “loud” sites. Sound sources included airplane (n = 2), construction (n = 6), energy development (n = 17), roadway (n = 52), urban (n = 101), and white noise (n = 3). We also distinguished study designs as event-based (n = 41) versus continuous (n = 140). Event-based study designs evaluated instantaneous signal flexibility in the presence of anthropogenic sound (e.g., a grasshopper calling more loudly during an airplane overflight compared to normal conditions, Fig. 2). Continuous study designs, on the other hand, evaluated differences in acoustic properties between populations in loud and quiet environments (e.g., communication characteristics of red-winged blackbirds, (Agelaius phoenicus), in rural versus urban environments; Fig. 2). Following our literature search, we incorporated a specific search for bat studies, as they were underrepresented in our initial search and we felt that they are good models for the study of anthropogenic sound impacts due to their reliance on acoustic information for both communication and foraging.

Analysis

To assess potential biases in the noise pollution literature, we assessed observed versus expected proportions of studies using Pearson’s χ2 tests. We conducted these tests to analyze numbers of studies for each response variable, sound source, focal taxa, continent, and study design; in each case we tested a null hypothesis that an equal proportion of studies have been conducted for each category (e.g., 50% of studies each for event-based and continuous study designs). To control the Type I error rate, we employed a Holm’s Sequential Bonferroni correction.

We conducted a meta-analysis to assess wildlife responses to noise pollution using the metafor package (Viechtbauer, 2010) in the R statistical environment (version 3.4.1, R Core Team 2017). We ran mixed-effects meta regression models with study design (event-based versus continuous), and taxa as fixed effects and study ID as a random effect.

When possible, we calculated Hedge’s g for each study that used a categorical noise treatment. When studies evaluated responses to noise along a continuous gradient, we calculated Hedge’s g using Pearson’s r. To evaluate overall effect of each response variable (Minimum Frequency, Maximum Frequency, Peak Frequency, Duration, Rate, and Amplitude), as well as the effect of study type and taxa, we evaluated overlap of 95% confidence intervals with zero. After conducting analyses, we constructed Q-Q plots to visually assess model fit.

The 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration NoisyMeasurement File (2023-04-03) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File. The NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the redistricting data portion of the 2010 Demonstration Data Products Suite – Redistricting and Demographic and Housing Characteristics File – Production Settings (2023-04-03). These statistical queries, called “noisy measurements” were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016] https://arxiv.org/abs/1605.02065; see also Dwork C. and Roth, A. [2014] https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] https://arxiv.org/abs/2004.00010), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Redistricting Data (P.L.94-171) Demonstration Noisy Measurement File (2023-04-03) has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004). The data includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product- planning/2010-demonstration-data-products/04 Demonstration_Data_Products_Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census. The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints—information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) —are provided.

This is a collection of data sets acquired for measurements of noise figure and receive system noise of wireless/radio frequency receivers and transceivers. These data include tabular data that list1) Inputs: calibrated input signal and excess noise levels, and2) Outputs: summary statistics for each type of user data collected for each DUT.The experiments that produced these data were meant to be used to assess noise measurands, but the data are generic and could be applied to other problems if desired.The structure of each zip archive dataset is as follows:| Root|-- (Anonymized DUT name 1)|---- Data file 1|---- Data file 2|---- ...Data file N|---- DUT-README.txt|-- (Anonymized DUT name 2)|---- Data file 1|---- Data file 2|---- ...Data file N|---- DUT-README.txt| (etc.)Data tables in each archive are provided as comma-separated values (.csv), and the descriptive text files are ASCII (.txt). Detailed discussion of the test conditions and data formatting is given by the DUT-README.txt for each DUT.

This dataset is called by the script "Modeling Rural Mobility in Burkina Faso.Rmd", which is available on Github: https://github.com/hrmeredith12/Rural-mobility-models.git

1. Environmental noise from anthropogenic and other sources affects many aspects of animal ecology and behaviour, including acoustic communication. Acoustic masking is often assumed in field studies to be the cause of compromised communication in noise, but other mechanisms could have similar effects. 2. We tested experimentally how background noise disrupted the response to conspecific alarm calls in wild superb fairy-wrens, Malurus cyaneus, assessing the effects of acoustic masking, distraction and changes in vigilance. We first examined the birds’ response to alarm-call playbacks accompanied by different amplitudes of background noise that overlapped the calls in acoustic frequency. We then scored and videoed their response to alarm calls in two types of background noise, that did or did not overlap call frequency, but were broadcast at a constant amplitude. 3. Birds were less likely to flee to alarm calls in higher amplitudes of overlapping noise, demonstrating that noise itself com...

This dataset is about books. It has 1 row and is filtered where the book is Acoustic echo and noise control : a practical approach. It features 7 columns including author, publication date, language, and book publisher.

This data set contains call record data from the 311 call center in Kansas City, MO. This dataset used to be published under the name "KCMOPS311". This name was changed to make the dataset name more reflective of it's contents.

NOTE: This data does not present a full picture of 311 calls or service requests, in part because of operational and system complexities associated with remote call taking necessitated by the unprecedented volume 311 is handling during the Covid-19 crisis. The City is working to address this issue.

All 311 Service Requests from 2010 to present. This information is automatically updated daily.

A. SUMMARY This dataset consists of San Francisco International Airport (SFO) aircraft noise reports and if available, the correlated flight operation’s details for those reports.

B. HOW THE DATASET IS CREATED SFO Noise Office collects this information via the WebTrak, Web, App, Complaint Hotline, emails, letters, and telephone calls to our office. This information is compiled and published in a monthly report which is presented at the SFO Airport Community Roundtable meetings (https://sforoundtable.org/). It serves to help understand community's aircraft noise concerns, to collaborate with all stakeholders in an effort to reduce and manage aircraft noise.

C. UPDATE PROCESS Data is available starting in August 2019 and will be updated on a monthly basis.

D. HOW TO USE THIS DATASET This data is the data source used to produce the Noise Reports section on page 5 of the monthly Airport Director’s Report. These reports are available online at https://www.flysfo.com/about/community-noise/noise-office/reports/airport-directors-report

E. RELATED DATASETS Previously provided data, Aircraft Noise Complaint Data, from January 2005 to July 2019 is available here: https://data.sfgov.org/Transportation/Aircraft-Noise-Complaint-Data/q3xd-hfi8

Please contact the Noise Abatement Office at NoiseAbatementOffice@flysfo.com for any questions regarding this data.

Females of many species choose mates using multiple sensory modalities. Multimodal noise may arise, however, in dense aggregations of animals communicating via multiple sensory modalities. Some evidence suggests multimodal signals may not always improve receiver decision-making performance. When sensory systems process input from multimodal signal sources, multimodal noise may arise and potentially complicate decision-making due to the demands on cognitive integration tasks. We tested female túngara frog, Physalaemus (=Engystomops) pustulosus, responses to male mating signals in noise from multiple sensory modalities (acoustic and visual). Noise treatments were partitioned into three categories: acoustic, visual, and multimodal. We used natural calls from conspecifics and heterospecifics for acoustic noise. Robotic frogs were employed as either visual signal components (synchronous vocal sac inflation with call) or visual noise (asynchronous vocal sac inflation with call). Females expre...

Malaria is the leading cause of death in the African region. Data mining can help extract valuable knowledge from available data in the healthcare sector. This makes it possible to train models to predict patient health faster than in clinical trials. Implementations of various machine learning algorithms such as K-Nearest Neighbors, Bayes Theorem, Logistic Regression, Support Vector Machines, and Multinomial Naïve Bayes (MNB), etc., has been applied to malaria datasets in public hospitals, but there are still limitations in modeling using the Naive Bayes multinomial algorithm. This study applies the MNB model to explore the relationship between 15 relevant attributes of public hospitals data. The goal is to examine how the dependency between attributes affects the performance of the classifier. MNB creates transparent and reliable graphical representation between attributes with the ability to predict new situations. The model (MNB) has 97% accuracy. It is concluded that this model outperforms the GNB classifier which has 100% accuracy and the RF which also has 100% accuracy. Methods Prior to collection of data, the researcher was be guided by all ethical training certification on data collection, right to confidentiality and privacy reserved called Institutional Review Board (IRB). Data was be collected from the manual archive of the Hospitals purposively selected using stratified sampling technique, transform the data to electronic form and store in MYSQL database called malaria. Each patient file was extracted and review for signs and symptoms of malaria then check for laboratory confirmation result from diagnosis. The data was be divided into two tables: the first table was called data1 which contain data for use in phase 1 of the classification, while the second table data2 which contains data for use in phase 2 of the classification. Data Source Collection Malaria incidence data set is obtained from Public hospitals from 2017 to 2021. These are the data used for modeling and analysis. Also, putting in mind the geographical location and socio-economic factors inclusive which are available for patients inhabiting those areas. Naive Bayes (Multinomial) is the model used to analyze the collected data for malaria disease prediction and grading accordingly. Data Preprocessing: Data preprocessing shall be done to remove noise and outlier. Transformation: The data shall be transformed from analog to electronic record. Data Partitioning The data which shall be collected will be divided into two portions; one portion of the data shall be extracted as a training set, while the other portion will be used for testing. The training portion shall be taken from a table stored in a database and will be called data which is training set1, while the training portion taking from another table store in a database is shall be called data which is training set2. The dataset was split into two parts: a sample containing 70% of the training data and 30% for the purpose of this research. Then, using MNB classification algorithms implemented in Python, the models were trained on the training sample. On the 30% remaining data, the resulting models were tested, and the results were compared with the other Machine Learning models using the standard metrics. Classification and prediction: Base on the nature of variable in the dataset, this study will use Naïve Bayes (Multinomial) classification techniques; Classification phase 1 and Classification phase 2. The operation of the framework is illustrated as follows: i. Data collection and preprocessing shall be done. ii. Preprocess data shall be stored in a training set 1 and training set 2. These datasets shall be used during classification. iii. Test data set is shall be stored in database test data set. iv. Part of the test data set must be compared for classification using classifier 1 and the remaining part must be classified with classifier 2 as follows: Classifier phase 1: It classify into positive or negative classes. If the patient is having malaria, then the patient is classified as positive (P), while a patient is classified as negative (N) if the patient does not have malaria.
Classifier phase 2: It classify only data set that has been classified as positive by classifier 1, and then further classify them into complicated and uncomplicated class label. The classifier will also capture data on environmental factors, genetics, gender and age, cultural and socio-economic variables. The system will be designed such that the core parameters as a determining factor should supply their value.

Description

This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions (MIMII DUE). The dataset consists of normal and abnormal operating sounds of five different types of industrial machines, i.e., fans, gearboxes, pumps, slide rails, and valves. The data for each machine type includes six subsets called ``sections'', and each section roughly corresponds to a single product. Each section consists of data from two domains, called the source domain and the target domain, with different conditions such as operating speed and environmental noise. This dataset is a subset of the dataset for DCASE 2021 Challenge Task 2, so the dataset is entirely the same as data included in the development dataset and additional training dataset. For more information, please see this paper and the pages of the development dataset and the task description for DCASE 2021 Challenge Task 2.

Baseline system

Two simple baseline systems are available on the Github repositories [URL] and [URL]. The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.

Conditions of use

This dataset was made by Hitachi, Ltd. and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Publication

If you use this dataset, please cite the following paper:

Ryo Tanabe, Harsh Purohit, Kota Dohi, Takashi Endo, Yuki Nikaido, Toshiki Nakamura, and Yohei Kawaguchi, "MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions," arXiv preprint arXiv: 2105.02702, 2021. [URL]

Feedback

If there is any problem, please contact us:

• Ryo Tanabe, ryo.tanabe.rw.xk@hitachi.com
• Yohei Kawaguchi, yohei.kawaguchi.xk@hitachi.com

1. Overview Format: 8kHz 16bit, wav, mono channel

Recording condition: Phone recording system, with low background noise (call center scenario)

Recording content: Spontaneous inbound and outbound callings in typical domain, such as finance, real-estate, sale, health, insurance, telecom

Language: English, German, French, Spanish, Italian, Portuguese, Korean, Japanese, Hindi, Arabic, Dutch, Swedish, Norwegian and etc.

Features of annotation: Transcription text, timestamp, speaker ID, gender, noise, PII redacted Accuracy: Word Accuracy Rate (WAR) 98%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 1 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go Machine Learning (ML) Data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/speechrecog?source=Datarade