There were estimated to be approximately 51,900 musicians working in the United Kingdom as of the fourth quarter of 2024, compared with 45,300 in the previous quarter.

Graph and download economic data for Employed full time: Wage and salary workers: Musicians, singers, and related workers occupations: 16 years and over (LEU0254485700A) from 2000 to 2019 about musicians, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

Streaming accounted for 84 percent of the U.S. music industry's revenue in 2024, up from 65 percent in 2017 and marking an increase of nearly 20 percent in that period. During the same time period, the share of revenue generated by digital downloads decreased significantly.

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}
}

This collection includes a comma-separated file (.csv) containing musician's dates of birth, dates of number one hit, and dates of death; a data file for the R software (RData file) to accompany the .csv file; R syntax to create a Lexis diagram displaying all data by calendar time and age at death; and R syntax to create death rates and musicians at risk figures. Data were obtained from Wikipedia, using the lists of number one albums by decade, starting with 1956 and ending at 2007. Using this data, researchers investigated whether famous musicians are at an increased risk of death at age 27, thereby testing the "27 club" hypothesis.

This statistic shows the share of independent music makers who have ever experienced stress, anxiety or depression in relation to their music creation worldwide as of April 2019, sorted by age group. The data reveals that younger music artists were more likely to have experienced some kind of mental health problems, with 80 percent of independent musicians aged between 18 and 25 years old saying they had suffered from stress, anxiety and/or depression in relation to their music creation. Conversely, 49 percent of musicians aged 46 or above said the same.

The total sales of recordings based on nationality of artists 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.

Financial overview and grant giving statistics of Musicians for Music

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.

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   |

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.

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.

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.

INTRODUCTION: Research has shown that hearing loss in musicians may cause difficulty in timbre recognition and tuning of instruments. AIM: To analyze the hearing thresholds from 250 Hz to 16,000 Hz in a group of music students and compare them to a non-musician group in order to determine whether high-frequency audiometry is a useful tool in the early detection of hearing impairment. METHODS: Study design was a retrospective observational cohort. Conventional and high-frequency audiometry was performed in 42 music students (Madsen Itera II audiometer and TDH39P headphones for conventional audiometry, and HDA 200 headphones for high-frequency audiometry). RESULTS: Of the 42 students, 38.1% were female students and 61.9% were male students, with a mean age of 26 years. At conventional audiometry, 92.85% had hearing thresholds within normal limits; but even within the normal limits, the worst results were observed in the left ear for all frequencies, except for 4000 Hz; compared to the non-musician group, the worst results occurred at 500 Hz in the left ear, and at 250 Hz, 6000 Hz, 9000 Hz, 10,000 Hz, and 11,200 Hz in both the ears. CONCLUSION: The periodic evaluation of high-frequency thresholds may be useful in the early detection of hearing loss in musicians.

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

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 ...).

Snapshot img

Music Market Size 2025-2029

The music market size is forecast to increase by USD 184.69 billion, at a CAGR of 18.1% between 2024 and 2029.

The market is experiencing significant shifts, driven primarily by the increasing adoption of digital music platforms. This trend is transforming the way consumers access and engage with music, offering new opportunities for market players. However, the market landscape is not without challenges. Mergers and acquisitions, as well as strategic alliances among companies and new entrants, are intensifying competition. These developments are reshaping the competitive dynamics of the market. Concurrently, the persistent issue of illegal downloads and piracy continues to pose a significant challenge. This obstacle undermines the revenue potential of legitimate music streaming services, necessitating effective countermeasures from industry stakeholders. Companies seeking to capitalize on market opportunities and navigate challenges effectively must stay abreast of these trends and respond accordingly. Adapting to consumer preferences for digital music and collaborating with strategic partners can help businesses remain competitive and thrive in this evolving market.

What will be the Size of the Music Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, with dynamic interplays between various sectors shaping its landscape. String instruments, for instance, experience ongoing advancements in technology and design, while music promotion relies heavily on digital platforms for fan engagement. Live music events, a cornerstone of the industry, adapt to changing consumer preferences, integrating elements of podcast hosting and streaming services. Audio interfaces and studio equipment are essential tools for music production, with continuous innovation driving improvements in sound quality and functionality. Folk music, once considered a niche genre, now enjoys a resurgence in popularity, inspiring new collaborations and cross-genre experiments. Music theory, long a foundational aspect of music education, intertwines with digital music distribution and music therapy, expanding its applications. Electronic music, a genre born from technological innovation, continues to evolve, influencing music promotion and streaming platforms. Brass instruments, woodwind instruments, and other acoustic instruments maintain their relevance, often showcased in classical music performances and music festivals. Intellectual property rights and music licensing remain crucial, as music publishers and record labels navigate the complexities of digital distribution and streaming services. Talent scouting and artist management adapt to the ever-changing music industry, with A&R professionals utilizing digital tools and social media for discovering new artists. Music history, a vital component of music education, intersects with the marketing and music merchandising, offering unique opportunities for fan engagement and monetization. Sound design, midi controllers, and effects processors contribute to the art of music production, with electronic music and hip hop genres leading the way in innovation. Music therapy, a burgeoning field, integrates music into various therapeutic applications, highlighting its transformative power. In the realm of music promotion, the marketing strategies evolve, with tour management and artist management adapting to the digital age. Music genres continue to blend and evolve, reflecting the diverse and dynamic nature of the market.

How is this Music Industry segmented?

The music industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userIndividualCommercialSourceRecordingLiveLicensingDistribution ChannelPhysicalDigitalLive EventsGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyThe NetherlandsUKMiddle East and AfricaUAEAPACChinaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By End-user Insights

The individual segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth, driven by advancements in technology and consumer preferences. Music streaming platforms have become increasingly popular, with mobile data traffic surging due to the proliferation of 4G and 5G networks. This trend is particularly notable in the individual user segment, which is expected to dominate the market. Music licensing, a key component of the market, is benefiting from the rise of streaming services and podcast hosting. Digital music distribution and music education are also thriving, with music notation software, mu

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.

The Music Market report segments the industry into Revenue Generation Format (Streaming, Digital (Except Streaming), Physical Products, Performance Rights, Synchronization Revenues) and Geography (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa). Get five years of historical data alongside five-year market forecasts.

Introduction

Generative AI in music refers to the application of artificial intelligence techniques, specifically generative models and algorithms, to compose, produce, and generate music autonomously. Generative AI models can analyze vast amounts of musical data, learn patterns, and create new melodies, harmonies, and rhythms that closely resemble human-created music. This technology has revolutionized the music industry by offering new creative possibilities, automating music composition, and providing personalized music experiences.

According to Market.us, The global Generative AI in Music market was valued at USD 229 million in 2022 and is expected to reach USD 2,660 million by 2032, with a remarkable CAGR of 28.6%.

The generative AI in music market has witnessed substantial growth in recent years, driven by several factors. Firstly, advancements in deep learning algorithms and neural networks have significantly improved the capabilities of generative AI models in understanding and creating music. These models can now generate complex musical compositions, experiment with various genres and styles, and even collaborate with human musicians.

Secondly, the demand for personalized music experiences and unique content has fueled the adoption of generative AI in the music industry. Music streaming platforms, production companies, and artists are leveraging generative AI to create personalized playlists, generate background music for videos or advertisements, and compose original pieces tailored to specific moods or preferences.

Furthermore, the efficiency and productivity offered by generative AI in music production have been driving factors for its market growth. Generative AI models can compose music at a rapid pace, reducing the time and resources required for traditional music composition. This scalability and efficiency allow musicians and composers to explore a wider range of musical ideas and output, leading to increased productivity and creativity.

One of the key opportunities in the generative AI in music market is the ability to automate music composition and generate a vast amount of original content. Generative AI models can analyze large datasets of existing music, learn patterns and structures, and generate new compositions that align with specific genres, moods, or artist styles. This opens up possibilities for musicians and composers to explore new creative territories and expand their musical repertoire.

Moreover, generative AI in music enables personalized music experiences for listeners. By leveraging user data, preferences, and contextual information, generative AI models can create personalized playlists, generate music that suits individual moods or activities, and offer tailored recommendations to enhance the music discovery process. This personalization enhances user engagement and satisfaction, leading to increased user retention and loyalty.

Additionally, the integration of generative AI with other technologies, such as virtual reality (VR) and augmented reality (AR), presents new opportunities for immersive music experiences. Generative AI can be used to create interactive and dynamic soundscapes, virtual concerts, and personalized audiovisual experiences, enhancing the overall music consumption and live performance aspects.

Furthermore, the generative AI in music market offers opportunities for collaboration between human musicians and AI models. Musicians can leverage generative AI ...