Streaming accounted for 84 percent of the U.S. music industry's revenue in 2023, up from 79 percent five years earlier and marking an increase of nearly 20 percent from 2017. During the same time period, the share of revenue generated by digital downloads more than halved.

This dataset provides a list of lyrics from 1950 to 2019 describing music metadata as sadness, danceability, loudness, acousticness, etc. We also provide some informations as lyrics which can be used to natural language processing.

The audio data was scraped using Echo Nest® API integrated engine with spotipy Python’s package. The spotipy API permits the user to search for specific genres, artists,songs, release date, etc. To obtain the lyrics we used the Lyrics Genius® API as baseURL for requesting data based on the song title and artist name.

According to data on the number of album shipments in the United States in 2023, a total of 37 million CD albums and 20.5 million digital album downloads were shipped. Nonetheless, these figures are both down from the 2022 shipment numbers, when the CD shipments and digital album download shipments amounted to 37.7 million and 24.5 million respectively.

MGD: Music Genre Dataset

Over recent years, the world has seen a dramatic change in the way people consume music, moving from physical records to streaming services. Since 2017, such services have become the main source of revenue within the global recorded music market.
Therefore, this dataset is built by using data from Spotify. It provides a weekly chart of the 200 most streamed songs for each country and territory it is present, as well as an aggregated global chart.

Considering that countries behave differently when it comes to musical tastes, we use chart data from global and regional markets from January 2017 to December 2019, considering eight of the top 10 music markets according to IFPI: United States (1st), Japan (2nd), United Kingdom (3rd), Germany (4th), France (5th), Canada (8th), Australia (9th), and Brazil (10th).

We also provide information about the hit songs and artists present in the charts, such as all collaborating artists within a song (since the charts only provide the main ones) and their respective genres, which is the core of this work. MGD also provides data about musical collaboration, as we build collaboration networks based on artist partnerships in hit songs. Therefore, this dataset contains:

• Genre Networks: Success-based genre collaboration networks
• Genre Mapping: Genre mapping from Spotify genres to super-genres
• Artist Networks: Success-based artist collaboration networks
• Artists: Some artist data
• Hit Songs: Hit Song data and features
• Charts: Enhanced data from Spotify Weekly Top 200 Charts

This dataset was originally built for a conference paper at ISMIR 2020. If you make use of the dataset, please also cite the following paper:

Gabriel P. Oliveira, Mariana O. Silva, Danilo B. Seufitelli, Anisio Lacerda, and Mirella M. Moro. Detecting Collaboration Profiles in Success-based Music Genre Networks. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), 2020.

@inproceedings{ismir/OliveiraSSLM20,
 title = {Detecting Collaboration Profiles in Success-based Music Genre Networks},
 author = {Gabriel P. Oliveira and 
      Mariana O. Silva and 
      Danilo B. Seufitelli and 
      Anisio Lacerda and
      Mirella M. Moro},
 booktitle = {21st International Society for Music Information Retrieval Conference}
 pages = {726--732},
 year = {2020}
}

Introduction

Amazon Music Statistics: Amazon Music is a popular music streaming platform, offering a variety of services, including Amazon Music Unlimited, Amazon Prime Music, and Amazon Music HD. As of 2023, Amazon Music boasts over 80 million songs in its catalog, providing a wide range of music options across genres. The service is available in more than 50 countries and is integrated with Amazon's smart devices, like Echo and Fire TV. Amazon Music Unlimited, the premium version of the service, offers access to an even larger selection of over 90 million songs. The platform also supports high-definition audio for subscribers of Amazon Music HD, with tracks available in lossless, CD-quality audio.

Amazon Music has seen steady growth, with recent reports suggesting that it has gained a significant share of the global streaming market, though it still trails behind competitors like Spotify and Apple Music. Additionally, Amazon Music offers personalized playlists and radio stations, enhancing the user experience through tailored recommendations. This article will discuss the important Amazon Music statistics and key trends.

The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of record production and integrated record production/distribution (NAICS 512210 & 512220), music publishers (NAICS 512230), sound recording studios (NAICS 512240), and other sound recording industries (NAICS 512290), annual, for five years of data.

The statistic provides data on favorite music genres among consumers in the United States as of July 2018, sorted by age group. According to the source, 52 percent of respondents aged 16 to 19 years old stated that pop music was their favorite music genre, compared to 19 percent of respondents aged 65 or above. Country music in the United States – additional information

In 2012, country music topped the list; 27.6 percent of respondents picked it among their three favorite genres. A year earlier, the result was one percent lower, which allowed classic rock to take the lead. The figures show, however, the genre’s popularity across the United States is unshakeable and it has also been spreading abroad. This could be demonstrated by the international success of (among others) Shania Twain or the second place the Dutch country duo “The Common Linnets” received in the Eurovision Song Contest in 2014, singing “Calm after the storm.”

The genre is also widely popular among American teenagers, earning the second place and 15.3 percent of votes in a survey in August 2012. The first place and more than 18 percent of votes was awarded to pop music, rock scored 13.1 percent and landed in fourth place. Interestingly, Christian music made it to top five with nine percent of votes. The younger generation is also widely represented among country music performers with such prominent names as Taylor Swift (born in 1989), who was the highest paid musician in 2015, and Hunter Hayes (born in 1991).

Country music is also able to attract crowds (and large sums of money) to live performances. Luke Bryan’s tour was the most successful tour in North America in 2016 based on ticket sales as almost 1.43 million tickets were sold for his shows. Fellow country singer, Garth Brooks, came second on the list, selling 1.4 million tickets for his tour in North America in 2016.

MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs.

To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review.

The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues.

MuMu dataset (mapping, metadata, annotations and text reviews)

Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments

These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus.

NOTE: This version provides simplified files with metadata and splits.

Scientific References

Please cite the following papers if using MuMu dataset or Tartarus library.

Oramas, S., Barbieri, F., Nieto, O., and Serra, X (2018). Multimodal Deep Learning for Music Genre Classification, Transactions of the International Society for Music Information Retrieval, V(1).

Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916

MusicOSet is an open and enhanced dataset of musical elements (artists, songs and albums) based on musical popularity classification. Provides a directly accessible collection of data suitable for numerous tasks in music data mining (e.g., data visualization, classification, clustering, similarity search, MIR, HSS and so forth). To create MusicOSet, the potential information sources were divided into three main categories: music popularity sources, metadata sources, and acoustic and lyrical features sources. Data from all three categories were initially collected between January and May 2019. Nevertheless, the update and enhancement of the data happened in June 2019.

The attractive features of MusicOSet include:

• Integration and centralization of different musical data sources
• Calculation of popularity scores and classification of hits and non-hits musical elements, varying from 1962 to 2018
• Enriched metadata for music, artists, and albums from the US popular music industry
• Availability of acoustic and lyrical resources
• Unrestricted access in two formats: SQL database and compressed .csv files

|    Data    | # Records |
|:-----------------:|:---------:|
| Songs       | 20,405  |
| Artists      | 11,518  |
| Albums      | 26,522  |
| Lyrics      | 19,664  |
| Acoustic Features | 20,405  |
| Genres      | 1,561   |

The sales based on format of musical recordings for the record production and integrated record production and distribution industries, sound recording and music publishing (NAICS 512210 and 512220), for two years of data.

This dataset represents real-time data collected from strategically placed microphones and sensors in a music education platform. It contains information about students' performances during music lessons and the associated teaching effectiveness. The dataset aims to simulate the analysis of musical performances, including the accuracy of pitch, rhythm, and dynamics, as well as the evaluation of student engagement and teacher feedback.

Features: Timestamp:

Description: The exact date and time when the data was collected. Type: DateTime Example: 2024-12-20 10:00:00 Sensor ID:

Description: Unique identifier for each microphone or sensor monitoring the performance. Type: Categorical Example: Sensor_001, Sensor_002 Student ID:

Description: Identifier for the student whose performance is being monitored. Type: Categorical Example: Student_001, Student_002 Instrument Type:

Description: The type of musical instrument being played during the lesson. Type: Categorical Example: Piano, Guitar, Violin Pitch (Hz):

Description: The frequency (in Hertz) of the sound produced by the instrument. Type: Numerical (Continuous) Example: 440 Hz (A4 note) Rhythm (BPM):

Description: The tempo of the music being played, measured in beats per minute (BPM). Type: Numerical (Continuous) Example: 120 BPM Dynamics (dB):

Description: The loudness or intensity of the sound produced by the instrument, measured in decibels (dB). Type: Numerical (Continuous) Example: 75 dB Note Duration (s):

Description: The length of time for which each note is held during the performance. Type: Numerical (Continuous) Example: 0.5 seconds Pitch Accuracy (%):

Description: The accuracy with which the pitch produced matches the intended pitch, expressed as a percentage. Type: Numerical (Continuous) Example: 95% Rhythm Accuracy (%):

Description: The accuracy with which the rhythm (tempo and timing) matches the intended pattern, expressed as a percentage. Type: Numerical (Continuous) Example: 100% Teaching Effectiveness Rating:

Description: A rating given to evaluate the effectiveness of the teacher’s instruction based on student performance. Type: Categorical (Ordinal) Example: 5/5, 4/5 Lesson Type:

Description: The type of lesson or session being conducted (e.g., beginner, advanced, or practice). Type: Categorical Example: Beginner Lesson, Advanced Lesson, Practice Session Student Engagement Level:

Description: The level of student engagement during the lesson, measured as either High, Medium, or Low. Type: Categorical Example: High, Medium, Low Teacher Feedback:

Description: Feedback provided by the teacher based on the student's performance. Type: Categorical Example: Good rhythm, Needs improvement, Excellent performance Environmental Factors:

Description: The environmental conditions in which the lesson is conducted, which could influence the quality of the performance data (e.g., background noise). Type: Categorical Example: Quiet, Slight Background Noise, Noisy Student Progress (%):

Description: A measure of student progress over time, expressed as a percentage of improvement in skills. Type: Numerical (Continuous) Example: 85%, 60% Target (Performance Evaluation):

Description: A classification based on performance score. Students are classified as either High Performance or Low Performance based on their pitch and rhythm accuracy. Type: Categorical Example: High Performance, Low Performance Target Column Definition: Performance Score: The average of pitch and rhythm accuracy. If the performance score exceeds 90%, the student is classified as High Performance; otherwise, they are classified as Low Performance.

This dataset was studied on Temporal Analysis and Visualisation of Music paper, in the following link:

       https://sol.sbc.org.br/index.php/eniac/article/view/12155

This dataset provides a list of lyrics from 1950 to 2019 describing music metadata as sadness, danceability, loudness, acousticness, etc. We also provide some informations as lyrics which can be used to natural language processing.

The audio data was scraped using Echo Nest® API integrated engine with spotipy Python’s package. The spotipy API permits the user to search for specific genres, artists,songs, release date, etc. To obtain the lyrics we used the Lyrics Genius® API as baseURL for requesting data based on the song title and artist name.

Gain valuable insights into music trends, artist popularity, and streaming analytics with our comprehensive Spotify Dataset. Designed for music analysts, marketers, and businesses, this dataset provides structured and reliable data from Spotify to enhance market research, content strategy, and audience engagement.

Dataset Features

Track Information: Access detailed data on songs, including track name, artist, album, genre, and release date. Streaming Popularity: Extract track popularity scores, listener engagement metrics, and ranking trends. Artist & Album Insights: Analyze artist performance, album releases, and genre trends over time. Related Searches & Recommendations: Track related search terms and suggested content for deeper audience insights. Historical & Real-Time Data: Retrieve historical streaming data or access continuously updated records for real-time trend analysis.

Customizable Subsets for Specific Needs Our Spotify Dataset is fully customizable, allowing you to filter data based on track popularity, artist, genre, release date, or listener engagement. Whether you need broad coverage for industry analysis or focused data for content optimization, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Trend Forecasting: Identify emerging music trends, genre popularity, and listener preferences. Artist & Label Performance Tracking: Monitor artist rankings, album success, and audience engagement. Competitive Intelligence: Analyze competitor music strategies, playlist placements, and streaming performance. AI & Machine Learning Applications: Use structured music data to train AI models for recommendation engines, playlist curation, and predictive analytics. Advertising & Sponsorship Insights: Identify high-performing tracks and artists for targeted advertising and sponsorship opportunities.

Whether you're optimizing music marketing, analyzing streaming trends, or enhancing content strategies, our Spotify Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

In 2023, approximately 142 million digital music singles were downloaded in the United States, down from just over 172 million a year earlier. Digital single downloads have dropped enormously in the last decade, and dropped below one billion in 2015 after a successful few years of growth.

This dataset is built by using data from Spotify. It provides a daily chart of the 200 most streamed songs for each country and territory it is present, as well as an aggregated global chart.

Considering that countries behave differently when it comes to musical tastes, we use chart data from global and regional markets from January 2017 to March 2022 (downloaded from CSV files), considering 68 distinct markets.

We also provide information about the hit songs and artists present in the charts, such as all collaborating artists within a song (since the charts only provide the main ones) and their respective genres, which is the core of this work. MGD+ also provides data about musical collaboration, as we build collaboration networks based on artist partnerships in hit songs. Therefore, this dataset contains:

Genre Networks: Success-based genre collaboration networks

Artist Networks: Success-based artist collaboration networks

Artists: Some artist data

Hit Songs: Hit Song data and features

Charts: Enhanced data from Spotify Daily Top 200 Charts

Financial overview and grant giving statistics of Music in Communities & Education

Financial overview and grant giving statistics of Music Time Learning Center Inc

Financial overview and grant giving statistics of Musicians for Music

This table contains 76 series, with data for years 2005 - 2011 (not all combinations necessarily have data for all years), and was last released on 2015-08-12. This table contains data described by the following dimensions (Not all combinations are available): Geography (6 items: Canada; Ontario; Quebec; Atlantic provinces ...), North American Industry Classification System (NAICS) (4 items: Record production and integrated record production/distribution; Sound recording studios; Other sound recording industries; Music publishers ...), Summary statistics (4 items: Operating revenue; Operating expenses; Operating profit margin; Salaries; wages and benefits ...).

This dataset contains fictional data for more than 12,000 songs across various genres, languages, and periods. It provides rich metadata such as song popularity, streaming statistics, and production credits. The dataset is designed for educational and creative purposes, offering insights into trends in music, listener preferences, and factors influencing song popularity.

Dataset Features

• song_id: A unique identifier for each song.
• song_title: The title of the song.
• artist: The name of the artist performing the song.
• album: The album where the song is featured.
• genre: The music genre (e.g., pop, electronic).
• release_date: The release date of the song (in DD/MM/YYYY format).
• Duration: The duration of the song in seconds.
• popularity: A score (1–100) representing the song's popularity.
• stream: The total number of streams the song has received.
• language: The primary language of the song.
• explicit_content: Indicates whether the song contains explicit content (yes/no).
• label: The record label that published the song.
• composer: The composer who wrote the song.
• producer: The producer responsible for the song's production.
• collaboration: This column shows about collaboration.

Distribution

• Data Volume: 12,794 rows and 15 columns.
• Format: Tabular dataset suitable for analysis in CSV, Excel, or database formats.

Usage

This dataset is ideal for a variety of applications:

• Music Trend Analysis: Explore genre trends, popularity metrics, and streaming behaviour.
• Recommendation Systems: Train machine learning models to suggest songs based on user preferences.
• Market Research: Analyze factors driving song popularity across genres and languages.
• Creative Projects: Use the data for storytelling, visualization, or mock product development.

Coverage

• Geographic Coverage: Global, representing songs in multiple languages and regions.
• Time Range: Includes songs from various time periods up to recent releases.
• Demographics: Covers multiple music genres, languages, and explicit content preferences.

License

CC BY-SA 4.0

Who Can Use It

• Data Scientists: To create models analyzing music trends or predicting popularity.
• Researchers: For academic studies on music and its cultural impact.
• Businesses: To analyze music market trends and consumer behavior.
• Content Creators: For storytelling, educational purposes, or prototyping music-related applications.