In 2024, approximately 122 million digital music singles were downloaded in the United States, down from just over 150 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.

The number of digital music album downloads in the United States amounted to **** million in 2024, marking a drop of more than ** percent from 2018. Over *** million digital music albums were downloaded in the U.S. year by year between 2011 and 2015, but the number then began to decrease annually and has failed to recover since.

Key Music Streaming App StatisticsTop Music Streaming AppsMusic Streaming RevenueMusic Revenue by FormatMusic Streaming MarketshareMusic Streaming Subscribers by AppMusic Streaming Users by AppMusic...

Market Size statistics on the Digital Music Downloads industry in the US

In 2024, more than **** million music downloads, either as single tracks or single/album bundles, were sold in Germany, according to the Federal Music Industry Association (Bundesverband Musikindustrie). This marked another decrease compared to the previous year.

"Top Songs of the World" is a collection of information about popular songs spanning various decades and genres. The dataset includes details such as the ranking of songs, the respective artists, titles, release years, sales figures, streaming statistics, download counts, radio play metrics, and a numerical rating. This dataset provides insights into the commercial success, digital presence, and overall popularity of each song, offering a comprehensive overview of the music industry's landscape over time. Researchers, analysts, and music enthusiasts can utilize this dataset to explore trends, patterns, and correlations within the context of the featured songs and artists.

This statistic displays the all-time official download top ten songs in the United Kingdom (UK) as of **********. The number one downloaded song in the UK was "Blurred Lines" with **** million sales, followed by "Someone Like You" by Adele with **** million sales.

Music Sales by Format and Year

Sales data for music industry by format and year

By Charlie Hutcheson [source]

About this dataset

The Music Industry Sales by Format and Year dataset provides comprehensive information on the sales data for different music formats over a span of 40 years. The dataset aims to analyze and visualize the trends in music industry sales, specifically focusing on various formats and metrics used to measure these sales.

The dataset includes several key columns to facilitate data analysis, including Format which represents the different formats of music sales such as physical (CDs, vinyl) or digital (downloads, streaming). Additionally, the column Metric indicates the specific measure used to quantify the sales data, such as units sold or revenue generated. The column Year specifies the particular year in which the sales data was recorded.

To provide a more comprehensive understanding of each combination of format, metric, and year, additional columns are included. The Number of Records column denotes the total number of entries or records available for each unique combination. This information helps assess sample size reliability for further analysis. Moreover, there is an Actual Value column that presents precise numerical values representing the actual recorded sales figure corresponding to each format-metric-year combination.

This dataset is obtained from credible sources including RIAA's U.S Sales Database and was originally presented through a visualization by Visual Capitalist. It offers insights into historical trends in music industry sales patterns across different formats over four decades.

In order to enhance this dataset visual representation and further explore its potential insights accurately, it would be necessary to perform an exploratory analysis assessing: seasonal patterns within each format; changes in market share across multiple years; growth rates comparison between physical and digital formats; etc. These analyses can help identify emerging trends in consumer preferences along with underlying factors driving shifts in market dynamics. Additionally,the presentation media (such as charts or graphs) could benefit from improvements such as clearer labeling, more detailed annotations,captions that allow viewers to easily interpret visualized information,and arrangement providing a logical flow conducive to understanding the data

How to use the dataset

Dataset Overview

The dataset consists of the following columns:

• Format: The format of the music sales, such as physical (CDs, vinyl) or digital (downloads, streaming).
• Metric: The metric used to measure the sales, such as units sold or revenue generated.
• Year: The year in which the sales data was recorded.
• Number of Records: The number of records or entries for each combination of format, metric and year.
• Value (Actual): The actual value of the sales for each combination of format, metric and year.

Key Considerations

Before diving into analyzing this dataset, here are some key points to consider:

• Categorical Variables: Both Format and Metric columns contain categorical variables that represent different aspects related to music industry sales.
• Numeric Variables: Year, Number of Records, and Value (Actual) are numeric variables providing chronological information about record counts and actual sale values.

Interpreting Insights

To make meaningful interpretations using this data set:

Analyzing Different Formats:

• You can compare different formats' popularity over time based on units sold/revenue generated.
• Explore how digital formats have influenced physical format sales over time.
• Understand which formats have experienced growth or decline in specific years.

Evaluating Different Metrics:

• Analyze revenue trends compared to unit count trends for different formats each year.
• Identify metrics showing exceptional growth/decline compared across differing years/formats.

Understanding Sales Trends:

• Examine the relationship between the number of records and actual sales value each year.
• Identify periods where significant changes in music industry sales occurred.
• Observe trends and fluctuations based on different formats/metrics.

Visualizing Data

To enhance your analysis, create visualizations using this dataset:

• Time Series Analysis: Create line plots to visualize the trend in music sales for different formats over time.
• Comparative Analysis: Generate bar charts or grouped bar plots...

This dataset was created using Spotify developer API. It consists of user-created as well as Spotify-curated playlists.
The dataset consists of 1 million playlists, 3 million unique tracks, 3 million unique albums, and 1.3 million artists.
The data is stored in a SQL database, with the primary entities being songs, albums, artists, and playlists.
Each of the aforementioned entities are represented by unique IDs (Spotify URI).
Data is stored into following tables:

• album
• artist
• track
• playlist
• track_artist1
• track_playlist1

album

| id | name | uri |

id: Album ID as provided by Spotify
name: Album Name as provided by Spotify
uri: Album URI as provided by Spotify

artist

| id | name | uri |

id: Artist ID as provided by Spotify
name: Artist Name as provided by Spotify
uri: Artist URI as provided by Spotify

track

| id | name | duration | popularity | explicit | preview_url | uri | album_id |

id: Track ID as provided by Spotify
name: Track Name as provided by Spotify
duration: Track Duration (in milliseconds) as provided by Spotify
popularity: Track Popularity as provided by Spotify
explicit: Whether the track has explicit lyrics or not. (true or false)
preview_url: A link to a 30 second preview (MP3 format) of the track. Can be null
uri: Track Uri as provided by Spotify
album_id: Album Id to which the track belongs

playlist

| id | name | followers | uri | total_tracks |

id: Playlist ID as provided by Spotify
name: Playlist Name as provided by Spotify
followers: Playlist Followers as provided by Spotify
uri: Playlist Uri as provided by Spotify
total_tracks: Total number of tracks in the playlist.

track_artist1

| track_id | artist_id |

Track-Artist association table

track_playlist1

| track_id | playlist_id |

Track-Playlist association table

- - - - - SETUP - - - - -

The data is in the form of a SQL dump. The download size is about 10 GB, and the database populated from it comes out to about 35GB.

spotifydbdumpschemashare.sql contains the schema for the database (for reference):
spotifydbdumpshare.sql is the actual data dump.

Setup steps:
1. Create database

- - - - - PAPER - - - - -

The description of this dataset can be found in the following paper:

Papreja P., Venkateswara H., Panchanathan S. (2020) Representation, Exploration and Recommendation of Playlists. In: Cellier P., Driessens K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science, vol 1168. Springer, Cham

Last.fm Limited is a music website founded in the United Kingdom in 2002. Using a music recommender system called "Audioscrobbler", Last.fm builds a detailed profile of each user's musical taste by recording details of the tracks the user listens to, either from Internet radio stations, or the user's computer or many portable music devices. This information is transferred ("scrobbled") to Last.fm's database either via the music player (including, among others, Spotify, Deezer, Tidal, MusicBee, SoundCloud, and Anghami) or via a plug-in installed into the user's music player. The data is then displayed on the user's profile page and compiled to create reference pages for individual artists.

The last.fm dataset consists of 166153 entries and 6 attributes. These attributes are: 1. Username: Consists of the name of the user. 2. Artist: Name of the artists that the user had heard. 3. Track: Consists of track/song name by that particular artist. 4. Album: Consists of names of the albums. 5. Date: Consists of the days ranging from January 1st to January 31st, 2021. 4. Time: Consists of the time of a particular day when the user had heard a particular track.

US Recorded Music Revenues by Format

US Recorded Music Revenues by Format in 2019 (Inflation Adjusted)

By Throwback Thursday [source]

About this dataset

This dataset contains comprehensive information about the US recorded music industry in 2019 Week 10. It includes details on the various formats of recorded music, such as CDs, vinyl records, digital downloads, and more. The dataset also provides data on the respective years in which these records were made, allowing for accurate historical comparison and analysis.

Key metrics provided include the number of units sold for each format, as well as corresponding revenue generated from their sales. In addition to the raw revenue figures, this dataset offers an extra column that presents inflation-adjusted revenue values. These adjusted figures take into account changes in purchasing power over time and enable a fair comparison of different years' revenues.

Overall, this dataset offers valuable insights into the US recorded music industry's performance in terms of format popularity and economic gains throughout a specific week in 2019. Researchers, analysts, and music professionals can utilize this comprehensive dataset to explore trends within specific formats while considering both absolute revenue and inflation-adjusted figures

How to use the dataset

Introduction:

• Understanding the Columns: a) Format: This column categorizes the format of the recorded music, such as CD, vinyl, digital download, etc. b) Year: This column represents the year in which the data was recorded. c) Units: The number of units sold for a particular format of recorded music. d) Revenue: The revenue generated from sales for a specific format. e) Revenue (Inflation Adjusted): The column that shows revenue adjusted for inflation.

• Analyzing Formats: By exploring and analyzing the Format column in this dataset, you can gain insights into changing consumer preferences over time. You can identify which formats have gained popularity or declined over different years or periods.

• Understanding Revenue Generation: To understand revenue patterns in relation to various formats and years, analyze both Revenue and Revenue (Inflation Adjusted) columns separately. Comparing these two columns will help you assess changes due to inflation accurately.

• Exploring Units Sold: The column Units provides insight into how many units were sold for each format within a specific year or period. Analyzing this data helps understand consumer demand across various formats.

• Calculating Inflation-Adjusted Revenue: Utilize the Revenue (Inflation Adjusted) column when analyzing long-term trends or comparisons across different periods without worrying about how inflation affects purchasing power over time.

• Comparing Multiple Years or Periods: This dataset includes information specifically for 2019 Week 10. However, you can use this dataset in conjunction with other datasets covering different years to compare revenue, units sold, and format performance across multiple years.

• Creating Visualizations: Visualizations such as line charts or bar graphs can help represent patterns and trends more comprehensively. Consider creating visualizations based on formats over multiple years or comparing revenue generated by different formats.

• Deriving Insights: Make use of the information provided to identify trends, understand customer preferences, and make informed decisions related to marketing strategies or product offerings in the music industry.

Conclusion:

Research Ideas

• Analyzing the impact of different music formats on revenue: This dataset provides information on the revenue and units sold for different recorded music formats such as CDs, vinyl, and digital downloads. By analyzing this data, one can identify which format generates the highest revenue and understand how consumer preferences have shifted over time.
• Tracking changes in purchasing power over time: The dataset includes both revenue and inflation-adjusted revenue figures, allowing for a comparison of how purchasing power has changed over the years. This can be useful in understanding trends in consumer spending habits or evaluating the success of marketing campaigns.
• Assessing market performance by year: With data on both units sold and revenue by year, this dataset can be used to assess the overall performance of the US recorded music industry over time. By comparing different years, one can identify periods of growth or decline and gain insights into factors driving these changes, such as technological advancements or shifts in consumer behavior

Acknowledgements

&...

Graph and download economic data for Sources of Revenue: Sale of Recordings for Music Publishers, All Establishments, Employer Firms (REVSLREF51223ALLEST) from 2013 to 2022 about recording, musical, printing, employer firms, accounting, revenue, establishments, sales, services, and USA.

In 2024, revenue from digital album downloads amounted to 166.7 million U.S. dollars, less than half the figure recorded in 2018. Meanwhile, subscription and streaming revenues have been increasing annually and reached 14.88 billion that same year, making up the vast majority of revenues for the entire music industry. Digital music – additional informationThe increase in digital music revenue, more specifically subscription and streaming services, may be down to the accessibility and availability of digital music. For legal downloads, consumers can pick between services such as Spotify, Apple Music, and Pandora. Of course, there are countless numbers of illegal sites which distribute digital music also. In 2019, approximately 34 percent of global internet users aged 16 to 24 admitted to accessing music through music ripping, the most popular method being copyright infringement.Digital music sales have made a huge impact on the listings of best-selling lists with digital sales either being combined with physical sales or set apart. The list of the top-selling digital songs in the United States in 2020 features artists such as The Weeknd, Tones and I, and Megan Thee Stallion. The title for the top selling digital song goes to ‘Blinding Lights’ by The Weeknd. The song sold over 372 thousand units in the United States from January to July 2020. Nevertheless, the share of the digital music market does not always directly correspond to the value of the digital market. The value of digital music singles downloads in 2024 amounted to 162.4 million U.S. dollars, which marked a drop in value from 678.5 million in 2017. The value of digital album sales also saw a decrease from 668.5 million in 2017 to 166.7 million in 2024.

The Digital Music Observatory has already created open-source software for the music industry that had been tested in real-life policy advocacy and business cases and scientific uses related to piracy research. While developed with a clear music industry focus, they have found thousands of users in the open research community worldwide for other purposes, too. We aim to further improve them to work as as a software ecosystem, and whenever possible, add web-based application interfaces with the ambition to make them useable for music organization that do not possess in-house R&D, IT, or data science capacities.

The datasets contains the download statistics of these packages from CRAN.

According to a survey conducted in South Korea in 2025, around ** percent of respondents stated that they had previously switched their music streaming or download service. The most common reason for this was because of expensive fees.

This dataset contains 80 pairs of song and their cover. These RAW wav files ares sourced from ISMIR https://www.ismir.net/resources/datasets/

Each of the songs in the pair is organized in a directory same as the name of the song title.

Download and extract the archive and read the README.md for covers80 package

This is a set of data and code to support the MIREX cover song identification task.

The directory covers32k/ contains 80 songs with two versions of each, all encoded as 32 kbps MP3 (mono, 16k sampling, bandwidth limited to 7 kHz). The basis of this set was cover versions .

Overview:

Dive into the vibrant world of Bollywood with our "🎵 Popular Spotify Hindi Hits Top 1000" dataset, a meticulously curated collection showcasing the most popular Hindi tracks lighting up Spotify. This dataset offers a unique lens into the musical preferences and trends within the realm of Hindi music on one of the world's leading streaming platforms. It's a celebration of rhythm and melody, capturing the essence of what makes Hindi music resonate with millions.

Data Science Applications:

This dataset is a treasure trove for music enthusiasts, data scientists, and researchers aiming to explore the dynamics of musical popularity, cultural influences in music, or algorithmic recommendations. With around 1000 entries, it's perfectly sized for in-depth analysis without overwhelming computational resources. Applications include: - Sentiment Analysis: Gauge the emotional tone and lyrical content of top tracks. - Trend Analysis: Identify emerging artists and music trends in the Hindi genre. - Recommendation Systems: Enhance music recommendation algorithms with popularity-based insights.

Please Note: This dataset is intended solely for educational and research purposes. Users are kindly asked to respect Spotify's guidelines and not use the data for commercial gains.

Column Descriptors:

• ID: Unique identifier for each track on Spotify.
• Name: Title of the track.
• Popularity: Spotify's popularity index for the track, indicative of its listening frequency.
• Artists: Names of the artists contributing to each track, capturing the collaborative spirit of music.

Ethical Data Mining:

This dataset was ethically mined using the Spotify API, adhering strictly to the platform's usage terms and respecting data privacy norms. It's a product of careful and responsible data collection practices, ensuring integrity and respect for the data source.

Acknowledgements:

We extend our heartfelt gratitude to Spotify for providing a robust platform that not only brings music from around the globe to our fingertips but also supports academic and research endeavors through its comprehensive API. This dataset stands as a testament to the power of music in bridging cultures and inspiring data-driven insights.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Music Instruments and Accessories in U.S. City Average (CUUR0000SERE03) from Dec 1997 to Sep 2025 about musical, instruments, urban, consumer, CPI, price index, indexes, price, and USA.

Survey on Equipment and Use of Information and Communication Technologies in Households: Internet use by socio-economic characteristics and frequency of download of music or films in the last three months. National.

In 2024, global music revenue generated by digital music and downloads continued its decline compared to recent years, at the level of *** billion U.S. dollars. It was the lowest figure reported since 2005. Music streaming is still in the lead The music industry has undergone fundamental changes due to the shifts in consumer demand and behavior. In 2024, global recorded music revenue hit an all-time high of around **** billion U.S. dollars, and while this figure was partially fueled by the revival of the live music sector, the top driver of growth was streaming. Music streaming accounted for an estimated ** percent of industry revenues in 2024, whereas digital downloads contributed less than ***** percent to the annual total. Audiences have come to prefer access models over ownership in recent years, which is no surprise considering the extensive range of titles available for a set rate on platforms like Spotify. Best-selling titles Most music lovers listen to their favorite tracks and discover new artists via streaming platforms. And yet, some fans also choose to pay for individual music purchases, be it via digital downloads or in physical formats. In 2023, Seventeen's “FML” was the best-selling music album worldwide, with over *** billion units sold. The K-pop band featured two of its albums in the same ranking that year. However, when it comes to vinyl, Taylor Swift led the ranks there. Her albums took up the top three spots on a list oftop-selling vinyl albums that same year.