DISCO-10M: A Large-Scale Music Dataset
Authors: Luca A Lanzendörfer
Florian Grötschla
Emil Funke
Roger Wattenhofer
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: music
dataset
machine learning
embedding
TL;DR: We present DISCO-10M, a novel and extensive music dataset that surpasses the largest previously available music dataset.

Abstract: Music datasets play a crucial role in advancing research in machine learning for music. However, existing music datasets suffer from limited size, accessibility, and lack of audio resources. To address these shortcomings, we present DISCO-10M, a novel and extensive music dataset that surpasses the largest previously available music dataset by an order of magnitude. To ensure high-quality data, we implement a multi-stage filtering process. This process incorporates similarities based on textual descriptions and audio embeddings. Moreover, we provide precomputed CLAP embeddings alongside DISCO-10M, facilitating direct application on various downstream tasks. These embeddings enable efficient exploration of machine learning applications on the provided data. With DISCO-10M, we aim to democratize and facilitate new research to help advance the development of novel machine learning models for music: https://huggingface.co/DISCOX

DISCO-10M: A Large-Scale Music Dataset
Luca A. Lanzendörfer
Florian Grötschla
ETH Zurich
ETH Zurich
Zurich, Switzerland
Zurich, Switzerland fgroetschlaßethz.coo
anzendoerfer@ethie
Emil Funke
Roger Wattenhofer
ETH Zurich
ETH Zurich
Zurich, Switzerland ef efunkedethz.ch
Zurich, Switzerland
wattenhofer@eth..co ch
Abstract
Music datasets play crucial role in advancing research in machine learning for music. However, existing music datasets suffer from limited size, accessibility, and lack of audio resources. To address these shortcomings, we present DISCO-10M, a novel and extensive music dataset that surpasses the largest previously available music dataset by an order of magnitude. To ensure high-quality data, we imple- ment a multi-stage filtering process. This process incorporates similarities based on textual descriptions and audio embeddings. Moreover, we provide precom- puted CLAP embeddings alongside DISCO- 10M, facilitating direct application on various downstream tasks. These embeddings enable efficient exploration of machine learning applications on the provided data. With DISCO-10M, we aim democratize and facilitate new research to help advance the development of novel machine learning models for music
Introduction
Music is a universal language that has captivated and inspired humans for millennia. With the rapid advancements technology, the domain of music has become increasingly interconnected with the field of machine learning, opening up new possibilities for music analysis, music recommendation systems, and the generation of novel compositions. Central to these developments are large and high-quality music datasets that learning models.
the foundation for training and evaluating modern machine
The recent breakthroughs achieved by large language models in the textual domain and diffusion models the visual domain have been largely attributed to the availability of massive datasets. These datasets have played pivotal role, enabling large models to learn intricate patterns and generate coherent output. However, in the realm of music, the availability of large-scale datasets has remained relatively limited, impeding the same advancements of research in this domain.
In the field of text processing, datasets such as Common Craw! and the Pile have made substantial amounts of written content available for training large language models. For instance, the Pile contains an extensive dataset 825 gigabytes of text, while CommonCrawl contains a staggering 3.3 terabytes of text. These datasets capture significant portion of the writing available on the Internet, providing an abundant resource for training and evaluating language models.
httpp://hbugiingffacc.c/////
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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Table 1: Comparison of public music datasets.
Dataset
# Clips
# Artists
Track duration
Year
Audio
GTZAN
1,000
300
30s
2002
yes
MagnaTagATunn
25,863
230
29s
2009
yes
MTG-Jamendo
55,609
3,565
full-length
2019
yes
Free Music Archive
106,574
16,341
full-length
2017
yes
Music4All
109,269
16,269
30s
2020
yes
Million Song Dataset
1,000,000
44.745
full-length
2011
no²
AudioSet
1,011,305
10s
2017
no²
AcousticBrainz
2,524.739
2015
DISCO-10M
15,296.232
400,047
full-length
2023
no²
Only 1,305 out of 2,084.320 clips are labeled Music.
Audio not directly available, can be downloaded from YouTube.
Similarly, in the visual domain, there exist various large datasets, such as the famous ImageNet [9 dataset, Open Images [19] Laion-400M [26). and Laion-5B [27] dataset, which contain massive amounts of images. These datasets have been instrumental in the advancements of computer vision research. Especially Laion-5B, with its five billion image-text caption pairs, has been essential for the success of current text-to-image generative models.
Contrary the textual and visual domains, existing music datasets face several limitations that hinder the progress of research in machine learning for music. One of the primary challenges is the limited size of available datasets, which restricts the diversity and representativeness of the musical content that can be analyzed, as well as the use-cases that can be tackled. Additionally, accessibility to these datasets has been concern, with many of the state-of-the-art models for audio and music being trained on proprietary datasets that are not accessible to the broader research community [TT, 37, 8 18 2 [7]. Moreover, the scarcity of available audio recordings poses a significant hurdle in obtaining datasets and, therefore, training novel machine learning models for music.
To address these shortcomings, we introduce DISCO-10M, a novel and extensive music dataset that surpasses the largest previously available music dataset by an order of magnitude. By curating DISCO- 10M, our objective is to overcome the limitations of existing datasets and provide researchers with rich and diverse collection of music data, enabling further advancements in the field of machine learning for music.
In addition the extensive music dataset, we also provide precomputed audio embeddings alongside DISCO-I0M 10M, which we obtain from pre- pre-trrined open-source CLAP model [35]. By including precomputed audio embeddings, we facilitate the direct application of machine learning models on various downstream tasks, eliminating the need for time-consuming audio retrieval and embedding computation, and reducing the barrier to entry for researchers the field.
The availability of DISCO- 10M aims to democratize access to large and high-quality music data for the research community. We envision that this dataset will serve catalyst for new research en- deavors, inspiring researchers to explore novel machine learning models, techniques, and applications in the domain of music. By advancing our understanding of music through machine learning, can foster the development of innovative music analysis tools, personalized recommendation systems, and creative music generation models.
2 Related Work
Music Datasets. We list some of the larger music datasets which have influenced our work. While GTZAN is small compared to other music datasets, it was one of the first datasets, originally intended for music genre recognition 32) The dataset contains 1000 files, each with a 30-second music clip. Every song belongs one of ten categories: Blues, Classical, Country, Disco, Hip Hop, Jazz, Metal, Pop, Reggae, and Rock. The data was gathered from various sources, including personal CDs, radio, and microphone recordings, and therefore varies greatly in quality. Furthermore, GTZAN does not

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provide other metadata, such as artist or album names. Even though GTZAN is small compared to more recent datasets, it remains a widely used dataset for music information retrieval tasks (T].
Magna'Tag ATune is a dataset that contains 25,863 music clips, each 29 seconds long 200 The music clips are extracted from 5223 unique songs across 230 artists. The dataset provides 188 tags for each music clip, crowd-sourced from ATune 2 AagATune is an online two-player game where each player creates tags for music clip. Afterward, players must decide based on both their created music lags if they were presented with the same music clip. If music clip receives the same tag from both players, the tag is added the music clip in the dataset.
Million Song Dataset (MSD) contains metadata and audio features for one million songs but does not contain the audio files 4 The dataset was initially created to bridge the gap between academia and industry by enabling academia to benchmark music information retrieval algorithms on industry-scale datasets.
AcousticBrainz is an open source and community-drit drivve database of 2.5 million music entries [22] While it is the largest music database, it does not distribute music or store links to third-party sites where the music can be found. Like MSD, AcousticBrainz stores audio features and metadata such as overall loudness, rhythm, genres, and instrumentation.
Free Music Archive (FMA) dataset was the first publicly available dataset to contain more than 100,000 freely available full-length music files across 16,000 artists 77]. The dataset is sourced from a freeform radio station of the same name which provides music released under the Creative Commons license. FMA provides fine-grained hierarchical genre metadata across 161 genres and contains high-quality audio.
AudioSet is a dataset containing two million YouTube video IDs with associated audio classes [[4]. AudioSet was created as dataset for audio event detection and to train and evaluate machine perception. Each dataset entry has a YouTube ID and a start and end timestamp. Furthermore, the dataset contains an audio label and short audio description. However, since we are specifically interested in music datasets, it should be noted that only one million IDs contain the music label. Similar MSD. the audio needs be downloaded from YouTube as is not provided with the dataset.
MTG-Jamendo introduced dataset for automatic music tagging (5. The data is subset of music sourced from the Jamendo website which contains royalty-free music. Jamendo offers music for commercial use, which means the music generally of higher quality. The dataset contains music tracks with 692 tag annotations, consisting of genre, instrument, and mood tags.
Music4All is dataset that contains 109,269 music clips across 16,269 artists PI. Each music clip is 30 seconds long and is cut from the middle of the original track. The dataset was created by collecting 15,602 users and their listening histories from last.fm The music from the listening histories was downloaded from Youeube and post-pree processedd by adding various features. The datasct contains 26 different features for each sample, such the music clips and tags from last.fm, audio features from Spotify, and lyrics from MusiXMatch
Embedding models. Learning latent representations of data has been an active field of research 33 (. One of the more recent advances in representation learning comes from language-image pre-training, where CLIP (Contrastive Language-Image Pre-training) model was trained on 400M pairs of language-image samples and obtained state-of-the-ar zero-shot classification results (24]. The resulting model learned a joint embedding space for both images and texts. This idea was extended to audio with CLAP (Contrastive Languagc-Audio Pre- training) [12]. We use Laion-CLAY (35.. open-source alternative to CLAP. Although could also embed text with CLAP, we decided to use only for audio. For text, we use Sentence BERT [25). Sentence BERT is trained by fine-tuning BERT model via siamese and triplet network to obtain semantically meaningful text embeddings 34
over the past decade 3 23 15
httops:/ttagatune.ooz
http:://freemssccarchivvee org/
https://www.lase fm/
https://www http://www.musixxee. www.mmuttcc.ccoommmmmm com/
3

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Track Meladata
Video Netadata Video tile
SportyDD Duration (mx]
Video desor
DISCO-1CM
Spotity
Dunedion
Top 10 backs
Explit
View Count
Video likk
Proviow
Duration
Track name
0.85
6g 0.25
or
Ege > 0.65
Fars also
lBoe
Seed wisi
AND
Lockup
proview
Spotty
Spoiily Artest Grapin
Figure 1: Overview of pipeline to create DISCO-10M Starting from a seed list of artists, we crawl the Spotify artist graph and collect related artists and their metadata. For each artist, we collect the metadata of up to ten "top tracks" and use them to search for up to twenty YouTube videos. The YouTube metadata, together with the Spotify metadata, are used to filter out datapoints according to their duration, title, and description similarity. The remaining matches are downloaded from Spotify and YouTube and compared. datapoint is discarded if the similarity is below the threshold.
Music Providers. We use Spotify, one of the most popular music, podcast, and audiobook streaming platforms, with 515 million users, 9 million artists, and more than 100 million available songs 28 30 Furthermore, we use YouTube, one of the largest on-demand video streaming and more than 500 hours of video uploaded every
platforms, with more than two billion monthly minute (36] YouTube offers a wide variety of content on its platform and allows anyone to upload their artistic creations to YouTube. This allows us to find audio samples for relatively unknown artists since the barrier to entry on YouTube is considerably lower than on Spotify. We use YouTube to expand our Spotify data with music videos and additional metadata.
3 Data Collection
Figure gives an overview of the data collection process for DISCO-10M. We compute a list of Spotify artists generated with breadth-fire first search over related artists, starting from seed list of artists that covers multiple genres. We then take the most popular songs for every artist, according to Spotify, and search for the songs on YouTube. At this point, we end up with a one-to-many mapping of songs to You Tube search results that we filter to improve the quality of the matches. In the first filtering step. we apply filters based on duration, the similarity between Spotify song title and YouTube title, and the similarity between the song title and the video description. We then download Spotify previews and YouTube videos for these prefiltered datapoints to generate CLAP audio embeddings. The similarity of these embeddings is used in the last filtering step. A full list of all metadata columns that we provide for DISCO- 10M can be found in Appendix B
3.1 Spotify Artist Graph
Our goal is obtain songs from diverse artists that span multiple genres. To decide which artists to consider, we start with a hand-curated list of seed artists (cf. Appendix A). We chose popular artists that are influential and represent their genre. To explore additional artists, we look for artists that "fans also like". feature provided by Spotify 22). Starting from the seed list, we explore these related artists one hop at a time, adding all related artists to the set of artists we already know. In other words, we obtain directed graph if we represent artists as nodes and add edges between them according to their related artist relationship. We now consider the nodes representing artists in seed list and run breadth-fin first search on this graph, discovering subset of all Spotify artists.

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Durstice san Barty
Sendariyy between euery and
Sesbufyy Neween query znd deson
Sendarity betwoon Fed enbodd
12
05
02
Figure 2: Distribution of data before each filtering step. We first filter the data according to duration similarity a). The data is then filtered if it is either below the title similarity threshold b) or below the description similarity threshold c). Finally, the data is filtered according to the audio similarity d).
Table 2: Examples for different levels of text similarity between search query and YouTuhe video title
Table Examples for differentt levels of text similariy between search query and YohTube video description
Search query
Tube video title
Text similarity
Search query
YouTube Decription Suippet
Test similarity
Take On Mc Superton
Bastar&Bass
Danelectro Doublenack
0.382
Higher Ally Brooke
Lises with headphours cat!
My name Deei, and I'm making
CLICK! Hello everyone!
0.115
Yourube Empry Arenz, Slowed..
Lead Mc
Eddie Neblett - Reset (Officill Lyrie Video)
Eddie Neblett
0.663
Feel You Tely Bramn
ftudy br deepooee
0.657
Che pyhare mambyry
Version polac Perfil
Che pyhare mambyry
(Version palac)
0.964
Nobody Knows When
You'de te Down And Out Scrapper Blackwell
Nelodd Knows You Whet You're Down and Out Scrapper Blackwell.
0,085
We stop after we find about 400,000 unique artists. While we collect this data, we also accumulate metadata provided by Spotify; this includes the artist name, artist music genres, artist popularity on Spotify, and the total number of Spotify followers for each artist in our list.
We take up to 10 "top tracks" per artist to sample songs for our dataset. The "top tracks" classification is provided by Spotify and is based on the listening behavior of users in the US market. We end up with approximately 2.5M unique songs and store metadata such as track name, album ID, artist ID, explicit flag, track duration, track release date, and a track preview URL. After collecting the song information, we can use to search for videos on YouTube with lookup containing the name of the song and the name of the artist.
3.2 Filtering
After the previous steps, we left with one-to-many mapping of Spotify songs to YouTTube search results. After cleanup of the data, we keep the following fields: the Spotify track ID, its corresponding track metadata, artist metadata from Spotify, and YouTube search result with its corresponding metadata.
When keeping all search results from YouTube, we have 46.8M matches. To improve the quality of the dataset and remove bad matches, we filter the dataset by duration similarity da, followed by text similarity 6yt and ,yd, and finally by audio similarity 8a. We empirically find that the following filtering thresholds work well:
(ōa > 0.25) A (6ye > 0.65 V 6gd > 0.65) A (6. 0.4)
Applying duration and text filtering stages removes approximately half of all entries from dataset, which results in the dataset containing 20M entries before audio similarity filtering. We filter the remaining entries based on their similarity between the Spotify preview and the YouTube audio embedding.
We demonstrate the effect of our filtering pipeline the data distribution in Figure In a), observe a small peak in the duration similarity at around 0.05. This is because You Tube search results contain relatively long videos compared to Spotify song duration. These long videos tend to be multi-hour versions of songs, full movies, or news broadcast live-streams. Therefore, we set the filtering cut-off in terms of duration such that YouTube videos are at most four times as long as the Spotify song.
The density distributions shown in Figure b), c) are the basis for the title and description filtering We found relatively strict filtering to work best as only keeps good matches and reduces the number of datapoints we have to process.
5

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Spocify
by YouTube
d, 0-2319
4, 0.8091
Figure 3: Comparison of audio similarity between Spotify preview audio and YouTube audio. 8 denotes the cosine similarity of the audio embedding. We use Log-Mel Spectrograms to visualize audio. We empirically observe that the similarity of our audio embeddings is related to the similarity of the Log-Mel Spectrograms and that the similarity increases when the spectrograms are closer to each other.
Using the OR operator in the comparison, we still allow some titles and descriptions with lower similarities to pass to the next filtering stage, long of the two similarities is high enough. Table provides examples of search queries and corresponding YouTube video titles, where matches with similarity scores above our threshold will continue the next stage. The same is shown in Table for YouTuue video descriptions.
Duration Similarity The duration similarity & is computed from the Spotify track duration and the length of the YouTube video ty as follows:
tyl
82 = max(ta bw)
where | denotes the absolute value. We chose this similarity metric because it intuitively captures the difference in audio duration, expressing the similarity as percentage difference between longer and shorter tracks. This allows for fine-granular filtering the data, where track l, and by have the same duration when 5a 1, and very dissimilar duration as 5 -> O.
Text Embedding Similarity We use the YouTube search query, the YouTube video title, and the YouTube video description to measure the similarity and thus the match of the resulting video compared our search query. All three texts are embedded into latent space using Sentence BERT 255. To measure the similarity of embeddings, we use a cosine similarity between the search query and the video title, denoted as &yt, and the cosine similarity between the search query and the video description, denoted as 5yd. This allows us to filter poor matches in our data before downloading Spotify previews and Youeube vidcos, which are used to compute the audio embeddings. We qualitatively compare text embedding matches for YouTube video titles in Table for YouTube descriptions in Table
Audio Embedding Similarity After applying text-based filters, we download Spotify audio track previews and YouTube videos to compute audio similarity and further filter the dataset. The pipeline to compute the audio embedding similarity for one datapoint is as follows: We download the Spotify

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Spotity duration
explict
explect
0.35
ubu duu nuilion
0.08
Fellae
True
classical
True
False
0.30
country
0.25
0.06
hiphup
Dordty
pop
0.04
olsco
0.15
6.30
blues
0.00
0.00
Duration [minutos]
Release
Donsity
Figure 4: Left Song duration for Spotify and YouTube after applying our filtering pipeline. The average track length is 3.5 minutes. We observe strong correlation between Spotify and Youtube track duration, in part due the duration similarity filtering. Middle Number of tracks and number of explicit tracks released per year. Right Genre distribution of GTZAN genres in our dataset. The genre embeddings were computed using a CLAP encoder on the sentence This audio is <genre> song. Each song is mapped to genre according to the largest cosine similarity between the song embedding and the genre embedding.
preview of the song together with the corresponding YouTube video. Both then fed into the Laion-CLan CLAP audio encoder model to produce an audio embedding for each audio sample. Once we have the embedding of the Spotify preview audio snippet and the YouTube audio, compute the cosine similarity of the embedding vectors. We denote the audio similarity as 5a-
We qualitatively evaluate the audio embedding and similarity scores in Figure 3 To compare the similarity between the two audio tracks, we use the perceptually relevant log-mel spectrogram. Since the Spotify preview is only 30 seconds, and the YouTube audio contains the entire track, we apply template matching algorithm to find the best overlapping spectrogram section of seconds in the YouTube audio. We observe increase in spectrogram similarity and, therefore, audio similarity as 5a increases.
3.3 Subsets
In addition to the DISCO- 10M dataset, propose several subsets with varying sizes and quality presets to enable fast prototyping as well as development with less powerful hardware.
DISCO-10K-random contains 10,000 random samples from DISCO- 10M. This split is mainly intended for prototyping and tasks that require substantially less data. It also enables training with fewer computational resources and hardware with memory limitations, even when all audio embeddings are loaded into memory.
MISCO-200-randdm contains 200,000 random samples and used for the data analysis in the following section. The dataset is small enough to remain managcable on common hardware while still being a statistically meaningful representation of our dataset.
contains selective subset of DISCO-100 with strict filtering based on duration, text, and audio similarity values. The matches of Spotify titles to YouTube videos are better than in DISCO-IOM resulting in a dataset that can be used for tasks where high-quality matches are required.
SECCO--0OK-high-gue
Data Analysis
We analyze the distributions of the duration of the song and the release year. Due our similarity filtering, duration distributions for Spotify and YouTube match each other, with an average song duration about 3.5 minutes. As the release dates are taken from Spotify metadata, they represent the date of the audio recording. The earliest date of recordings in our dataset is in the 1960s; while there are some individual songs that have an earlier date the metadata, we remove them outliers. We also observe that the number of songs released per year grows rapidly. This coincides with the
7

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Spotify embeddings
Youtube embeddings
5
5
t:SNE dimension
0
0
-5
-5
-10
-10
-5
10
-10
10
t-SNE dimension 1
Classical
Country
Pop
Reggae
Hiphop
Figure 5: t-SNE plots for Spotify preview embeddings and Youtube audio embeddings computed with CLAP. Colors represent GTZAN genres that were computed from the Spotify embeddings with zero-shot genre classification. The t-SNE plots show the relative positions of music samples, where samples with similar embeddings in the CLAP latent space are located closer to each other and dissimilar points farther apart. We observe that genres are well separated for both the YouTube embeddings and Spotify embeddings. Genres like classical music and metal lie far apart, whereas genres such as metal and rock lie close together.
proliferation of digital music. The cutoff date for dataset is Spring 2023. We plot song duration and release years for songs in DISCO-10M in Figure 4
To analyze the genre distribution in DISCO-10M, we utilize the precomputed CLAP embeddings of the 30-second Spotify previews for zero-shot genre classification. We do so by first computing text embeddings of genre prompts and then identifying the closest genre embedding using cosine similarity in the shared latent space for every song. We compute the genre text embeddings with the prompt: This audio is <genre> song. Note that the genre classification is based YouTube embeddings, of which multiple variations can exist per song, as DISCO-10M contains approximately 2.6M Spotify songs that mapped 15.3M music videos YouTube.
Figure shows the distribution of GTZAN genres on a representative slice of the dataset. Based on this classification, DISCO-10M is mainly composed of pop and disco music, but offers broad spectrum overall. We further depict the proportion of explicit songs for each genre. Hip-Hop songs have the highest percentage of explicit songs in dataset, at around 50%.
find that
To evaluate the quality of our genre classification, we compute t-SNE plots on the Spotify and Youtube embeddings. together with their assigned genre (see Figure We observe a separation of genres, where the overlap of neighboring genres is also reasonable, e.g., for metal and rock. Classical music is the most fragmented genre, whereas other genres build more contiguous areas. This aligns well with our expectations regarding the genre distribution, lending a certain degree of credibility.
Limitations and Ethical Discussion
We collected data from server located in Zurich, Switzerland, between January and June 2023. As search results are impacted by time and location, our search results are likely different from other locations, adding bias dataset and hindering reprodueibility. The artist graph obtained from Spotify also subject to changes, and our dataset represents a snapshot the graph during the mentioned time frame.
Furthermore, the dataset we distribute contains links to online resources for songs and videos that we do not control. These resources can change over time they may be removed replaced entirely
8

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by YouTube, Spotify, or their creators. However, since DISCO-10M can contain multiple matching YouTube results for single song, this redundancy helps counteract dataset degradation over time. Due to this redundancy, we may also include search results that might not represent the original song but something related to it. We believe these samples are still valuable, but users must be aware of this limitation. There is no guarantee for the correctness of the match between the Spotify track and the corresponding Yobeube video. For tasks where higher match quality is important, we provide the subset BBSSO--20KK-hih--que
The metadata for each song in our dataset is either produced by us (i.e., audio and text embeddings) or available online (i.e., song duration, video duration, view count, etc.). We only provide links to publicly available sources and do not own the copyright of any music referenced in the dataset.
Additionally, the list of artists collected by crawling the "fans also like" section on Spotify might be skewed towards certain genres, artists, or cultures, which can result in a biased representation of music. This can perpetuate stereotypes or hinder the inclusivity of diverse musical expressions.
Another concern stems from restrictions during the data collection process: We only access, download, and process YouTube videos that are not age-restricted. This can lead to some bias in the data, although it should be mentioned that not all Spotify songs that are marked as explicit are also restricted on YouTube. The explicit flags provided by Spotify and the exclusion of age-restricted YouTube videos allow for easier filtering of non harmfuul content, but cannot guarantee that the dataset does not contain hateful or offensive content after filtering. Therefore, in its current form, we strongly advise using this dataset for academic purposes only.
6 Conclusion
We introduce DISCO-I0M, an extensive and novel music dataset that surpasses existing music datasets by an order of magnitude. The data collection process involves leveraging popular songs from Spotify and identifying corresponding YouTube videos through careful search result filtering, ensuring the inclusion of high-quality data. To enhance the usability of the dataset, we provide embeddings for both song previews from Spotify and embeddings from YouTube videos, enabling researchers to apply their own thresholds for further filtering and facilitating efficient exploration of downstream tasks and data analysis. Our analysis demonstrates DISCO-10M'S broad coverage of songs from various genres.
Moreover, the paper highlights the limitations of the dataset and addresses the ethical impact associ- ated with its creation and usage. Recognizing the importance of ethical considerations, we encourage responsible and mindful utilization of DISCO- 10M, acknowledging the potential implications and ensuring that future research and applications derived from the dataset are conducted with care.
Overall, DISCO-10M represents a valuable resource for the research community, providing a large- scale music dataset that can be used to advance various aspects of music analysis, personalized recommendation systems, creative music generation models. its comprehensive coverage, com- bined with the availability of embeddings, contributes to the broader field of music research by democratizing access to large-scale music datasets.
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[17] Q. Huang, D. S. Park, T. Wang. T. I. Denk, A. Ly, N. Chen, Z. Zhang, Z. Zhang. J. Yu, C. Frank, J. Engel, Q. Le, W. Chan, Z. Chen, and W. Han. Noise2music: Text-conditioned music generation with diffusion models, 2023.
[18] F. Kreuk, G. Synnaeve, A. Polyak, U. Singer, A. D'efossez, J. Copet, D. Parikh, Y. Taigman, and Y. Adi. Audiogen: Textually guided audio generation. ArXiv, abs/2209.15352. 2022.
[19] A. Kuznetsova, H. Rom, N. G. Alldrin, J. R. R. Uijlings I. Krasin, J. Pont- S. Kamali, S. Popov, M. Malloci, A. Kolesnikov, T. Duerig, and V. Ferrari. The open images dataset v4. International Journal of Computer Vision, 128:1956-1981. 2018.
[20] E. Law, K. West, M. I. Mandel, M. Bay, and J. S. Downie. Evaluation of algorithms using games: The case of music tagging. In ISMIR, pages 387-392. Citeseer, 2009.
[21] I. A. Pegoraro Santana, F. Pinhelli, J. Donini, L. Catharin, R. B. Mangolin, Y. M. e. G. da Costa, V. Delisandra Feltrim, and M. A. Domingues. Music4all: A new music database and its applications. In 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), pages 399-404, 2020. doi: 10. 11L119////SSIP/889900000994411
[22] A. Porter, D. Bogdanov, R. Kaye. R. Tsukanov, and X. Serra. Acousticbrainz: A community platform for gathering music information obtained from audio. In International Society for Music Information Retrieval Conference, 2015.
[23] A. Radford, Metz, and Chintala. Unsupervised representation learning with deep convolu- tional generative adversarial networks. CoRR, abs/1511.00434, 2015.
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[24] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, Mishkin, J. Clark, G. Krueger, and Sutskever. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, 2021.
[25] N. Reimers and I. Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 11 2019. http:://ttttttt org/ abs/ 1908 10084
[26] C. Schuhmann, R. Vencu, R. Beaumont, R. Kaczmarczyk, C. Mullis, A. Katta, T. Coombes, J. Jitsev, and A. Komatsuzaki. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. ArXiv, abs/211 1221111022111 1.01 14, 2021.
[27] C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman, et al. Laion-5b; An open large-scale dataset for training next generation image-text models. arXiv preprint arXiv:2210.08400) 2022.
[28] Spotify, About spotify, URL /tt.tttpee//iieetrrroooooooooo/o httppp/ii....oooaaoo//eeetaaaaaa Spotify Terms of Service httpp//wwtt///////rrrrppprrrrorr
[29] Spotify. How "fans also like" works, URL httpes//arrisstt..poit.tyoo
fow-fans- -oans-also---k--orkke--
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[33] A. van den Oord, Y. Li, and O. Vinyals. Representation learning with contrastive predictive coding. ArXiv, hts/1807.03774g 2018.
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I. Polosukhin. Attention all you need, 2017.
[35] Y. Wu, K. Chen, T. Zhang, Y. Hui, T. Kink-Kikkaatrick, and S. Dubnov. Large-scale contrastive language-audio pretraining with feature fusion and seyword-to-capleee augmentation, 2023.
[36] YouTube.
Copyright transparency report. URL htgpicppss//sttrrgg.ooogggaa/// com/
srrrrpprr/ppporr--doonaaa/a/rdrr en_ mn_v1.pdf YouTube Terms of Service https:://ww. http://wwww.yuuuuu.cccooo
[37] N. Zeghidour, A. Luebs, A. Omran, J. Skoglund, and M. Tagliasacchi. Soundstream: An end-to-end neural audio codec. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30:495-5077 2022.

---

A Seed artists
We use the following artists as a seed list for the traversal of the artist graph on Spotify.
Saariingarrissn = [
# Metallica
3
# The Beatles
774As/4bee4XxuubB332zutr Bob Dylan
# The Weeknd
# Muddy Waters
77/60004451111000ssttvvlee Jimi Hendrix
# Johnny Cash
The Who
# Eminem
55PKCCCEE223JJJH22241111111111 Rihanna
# Kraftwerk
12xcd$5xxxx<3RE77777710011111111
# 2pac
0k6777N0D4PP1111bbbb0000111
# Miles Davis
*GtbjNWBEIzzz00BBBA1111P1'''
# Madonna
# Stevie Wonder
"20eyxaag{AAQ2233339900pp2;1; Bob Marley
# Johann Sebastian Bach
Sergei Rachmaninoff
]
B Columns in DISCO-10M Dataset
Dataset_columme [
griddoo_ur_yoututl
stkdeo_ttitl_yyuttuu
ttrack_name_spotiff""
"y.dee_duuratioon_oottuee_ee
"prerriev_url_spotfffy.
"rilee_vieer_ccmmt_ouutttee!
"search.query_youuubbe'''
wy.de__deccrppttoooooottttt
"track_id_spotiffy""
"album_id_spotifyy,,
"artiist_id_spotiffy"
"trackeduuratiionnspoittt.."'""
"primaary.artisst.aaeesppp..
"track_release.dtttessttt./
"erpliciit.contemmt..ppoi."
"similaritty.durationn"
"simmllarttyy.quuyyrrrreetttttt
sstaalaaittyyquery.ddrrrpp...o!
9 "similarityy_udio""
paddoo_emmbeddingsssppr.....
1 Baddio_ebbeddiin__oouuubb
]
C Additional Examples: Audio Similarity Comparison
We demonstrate results our audio similarity approach on five additional samples (see Figure 6). Similarly to the spectrograms presented in Section 3.2 we also observe overlap for these samples when the audio similarity above 8, 0.4. Even when two music snippets have the same frequency characteristics, there might still be small differences. This can be explained in part due to the audio
12

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quality of a YouTube video, which is dependent on the quality selected by the person uploading the video and can, therefore, vary greatly, unlike the audio quality of Spotify. This difference can be seen best in the high-frequency content of the spectrogram, which tends to be weaker and less pronounced in the YouTube audio samples. We notice a strong dissimilarity in the first example between the Spotify preview audio spectrogram and the YouTube audio spectrogram. This is reflected by the low similarity score ó, = 0.1985.
Spurify
YouTube
4,
-04028
04178
4 0.5060
4,
Figure 6: Comparison of audio similarity between Spotify preview audio and YouTube audio. 8 denotes the cosine similarity of the audio embedding. We observe that the similarity of our audio embeddings related to the similarity of the Log-Mel Spectrograms, and that the similarity increases when the spectrograms closer each other.
D FMA Genre Analysis
We repeat the zero-shot genre classification from Section 4 for the 16 FMA root genres. Figure 7 shows the genre distribution for the FMA genres, while Figure depicts the same t-SNE plot as shown in Figure for these genres. We can observe that our results between overlapping genres are consistent, and although there are more genres, we can observe meaningful relationships between them.
13

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explict
rock
True
country
eacy listening
electronic
pap
Genee
international
soul RnB
old-time/ / iistoric
blues
hip-hop
experimental
spoken
0.00
0.05
0.10
0.15
0.20
Figure Genre distribution of FMA root genres in our dataset. The genre embeddings were computed using a CLAP encoder on the sentence This audio is <genre> song. Each song is mapped to genre according to the largest cosine similarity between the song embedding and the genre embedding.
Spotify embeddings
Youtube embeddings
10
10
t-SNE dimension 2
-10
-10
-10
-5
10
-10
-5
10
t-SNE dimension
Classical
Easy listening
Pop
Old time / historic
Blues
Experimental
Rock
Electronic
International
Folk
Hip-hop
Spoken
Country
Instrumental
Soul-mb
Jazz
Figure 8: t-SNE plots Spotify preview embeddings and YouTube audio embeddings computed with CLAP. Colors represent FMA root genres that were computed from the Spotify embeddings with zero-shot genre classification. The t-SNE plots show the relative positions music samples, where samples with similar embeddings in the CLAP latent space are located closer to each other and dissimilar points farther apart. We observe that genres are well separated for both the Yubeube embeddings and Spotify embeddings.
14

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E
Subset Details
As described in Section we provide three different subsets of DISCO-IOM.
DISCO-10K-random contains 10,000 random samples from DISCO-1OM. We select the samples randomly from unique Spotify track IDs, meaning that the dataset will contain exactly YouTube video link per Spotify song.
DISCO-200K 2000-200-kighguuaait contains 200,000 high-quality samples filtered more strictly to improve the quality of the matches. We created this subset by filtering DISCO-10M with (6, > 0.7) A (6yt 0.8).
F
Audio Characteristics
The total playtime of YouTube videos in DISCO- 10M is approximately 1,062,604 hours or 121 years.
We provide further insights on the audio attributes of the sample rate, MP3 file bitrate, and number of channels based on the DISCO- 10K-random subset. When downloading Spotify previews in MP3 format, the audio quality and characteristics remain consistent. All audited samples share common attributes: a sample rate of 44.1 kHz, bitrate of 96 kbps, and 2-channel stereo setup.
In the case of audio from YouTube, there is a noticeable resemblance, albeit slightly less uniform. 99.78% of the examined videos employ a standard stereo 2-channel configuration. 0.18% of videos are mono channel, while 0.04% utilize 6-channel surround for audio output. 99.96% of videos have sample rate of 44.1 kHz, aligning with the settings of Spotify previews the remaining 0.04% deviate by having a sample rate of 48 kHz.
I Ethical Considerations
Copyright and licensing agreements complex issue, particularly when it comes to big data collection for training large machine learning models. We acknowledge the concerns of artists regarding the potential negative impact their artistic work. However, we believe that openly sharing such data helps democratize the research of music understanding and music creation.
To address artists who disagree with our assessment, we offer two options for reconciliation. First. artists can contact us to request the removal of links associated with their from our dataset Second, artists may choose to take down their YouTube video Spotify song from the respective platform, rendering the link contained in DISCO- invalid. It is important to note that our guarantee applies solely to our dataset, while other entities who hold private audio datasets may not offer the same level of control.
Creating dataset of this magnitude is achievable using publicly available tools and a reasonable timeframe. By making our dataset open-source, we also aim to raise awareness on the ease of creating big datasets and uncover the potential existence of similar datasets held by private institutions. Our goal is provide an opportunity for anyone interested enhance our understanding of music creation and safety with large datasets. We emphasize that this dataset serves as a starting point, pushing the boundaries and fostering research enhanced datasets for various tasks in machine learning for music. Access to such extensive datasets is crucial, not only the visual domain as demonstrated by Laion-5B, but also in the domain of music.
explore ideas with this dataset, and to
In summary, our ethical framework emphasizes the importance of respecting artists' concerns, providing options for data exclusion, promoting transparency in dataset creation, and facilitating meaningful exploration of ML-assisted music creation while prioritizing safety considerations.
"Contact via disco.dtasee@etth..co email
15

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Datasheet for Datasets
J.1 Motivation
1. For what purpase was the dataset created? Was there specific task in mind? Was there a specific gap that needed to be filled? Please provide description.
We want to provide an open- large-scale music dataset for the research com- munity. Such large datasets do not yet exist in this domain, and we believe they are needed to democratize innovation music research and ML-assiised music creation. Working with large data also has inherent risks, which are better analyzed openly by large community rather than by private institutions behind closed doors.
2. Who created the dataset (e-g., which team, research group) and on behalf of which entity (e.g,, company, institution, organization)?
The authors created the dataset part of their ongoing research at ETH Zurich.
3. Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.
N/A
4. Any other comments?
No.
J.2
Composition
1. What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide description.
We share 15,296,232 YouTuub links to music with metadata and associated Spotify music metadata. In addition, we provided similarity measures between the YouTube video title, the YouTube description, the Song title, and the name of the artist Addi- tionally, contribution includes providing audio embeddings for the YouTube video and the Spotify song preview computed with Laion-CLAP 35 The metadata includes an explicit flag to allow users to filter for explicit or non-explicit music.
2. How many instances are there total (of each type, if appropriate)?
15,296,232
3. Does the dataset contain all possible instances or is it sample (not necessarily random) of instances from a larger set? If the dataset is sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (c.g., to cover more diverse range instances, because instances were withheld unavailable).
No. DISCO-IOM does not cover all artists Spotify and only selection of popular songs of those that we do consider. The YouTube search results only contain 20 matches we take into consideration. To improve the dataset quality, we filter out matches that do not meet the threshold described in Section 3.2
4. What data does each instance consist of? "Raw" data (e.g., unprocessed text or images) features? In either case, please provide description.
URLs to YouTube videos and Spotify song previews as well as song-specific metadata, such as artist names, artist/song IDs, YouTube video title/descriptio snippet, video views, duration, Spotify song duration, and creation date
5. Is there label or target associated with each instance? If so, please provide description. No.
6. Is any information missing from individual instances? If so, please provide a description explaining why this information is missing (e.g. because it was unavailable). This does not include intentionally removed information but might include, e.g., redacted text.
16

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The artist names are not always known since we do not have this information for every artist ID our dataset. This is the case in 3.46% of all datapoints. In addition, we have 7.81 % missing YouTube description snippets and 0.183% missing YouTube view counts.
7. Are relationships between individual instances made explicit (e.g,, users' movie ratings, social network links)? If so, please describe how these relationships are made explicit.
No.
8. Are there recommended data splits (e.g., training, developmentvvalidation,, testing)? If
so, please provide description of these splits, explaining the rationale behind them.
No.
9. Are there any errors, sources of noise, or redundancies in the dataset?If so, please provide a description.
We acknowledge the existence of duplicate songs stemming from different You'll YouTube videos corresponding the same Spotify song. These duplicates can be removed by filtering with stricter thresholds (ef. Section 3.2)
10. Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies external resources, a) are there guarantees that they will exist and remain constant over time; b) are there official archival versions of the complete dataset (i.e., including the external resources existed at the time the dataset was created); c) are there any restrictions (e.g.. licenses, fees) associated with any of the external resources that might apply to dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, appropriate.
they
DISCO-10M relies the availability of the songs YouTube and Spotify since only link to those resources. The embeddings and other metadata are self-contained.
11. Does the dataset contain data that might considered confidential (e-g., data that is protected by legal privilege or by doctor- patient confidentiality, data that includes the content of individuals' non-pute non-public communications)? If so, please provide description.
No, there are no confidential datapoints in DISCO-10M.
12. Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why.
DISCO-1OM 10M contains music with an explicit flag. We do not know in what ways the song explicit (sexual, abusive, or others), ), but the flag allows users to filter for such songs easily. Additionally, DISCO- does not contain any links to age-restricted YouTube videos.
13. Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.
No.
14. Is it possible to identify individuals (i.e., one more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.
Yes, the Spotify artist ID is directly related one multiple natural persons. Addi- tionally, the YouTube video URLs we provide in the dataset are uploaded by one or multiple natural persons.
15. Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals race or ethnic origins, sexual orientations, religious beliefs, political opinions union member- ships, locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? If so, please provide description. No.
16. Any other comments?
17

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We emphasize the focus of our dataset on music and not on individuals. Additionally. we reiterate that this dataset intended for research purposes only, as described in Section 5
J.3 Collection Process
1. How was the data associated with each instance acquired? Was the data directly ob- servable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (c.g., part-of-speech tags, model-based guesses for age or language)? If the data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how.
The YouTube videos and metadata, and the Spotify tracks and metadata are observable and were collected by accessing the Spotify API as well as the YouTube API. The similarity scores and audio embeddings computed by
2. What mechanisms procedures were used to collect the data (e.g., hardware appara- tuses or sensors, manual human curation, software programs, software APIs)? How were these mechanisms procedures validated?
The Spotify API and the YouTube API. Our results were validated manually by assess- ing the quality of the retrieved information on random samples.
3. If the dataset is sample from a larger set, what was the sampling strategy (e.g.. deterministic, probabilistic with specific sampling probabilities)?
We started the Spotify artist scraping from the artist seed deseribed in Appendix A Additionally, we filter high-quality datapoints described in Section 3.2
4. Who was involved in the data collection process (e.g,, students, crowdworkers, contrac- tors), and how were they compensated (e.g., how much were crowdworkers paid)?
Only the authors of this paper were involved in the data collection process. Author involvement and payment disclosed after acceptance.
5. Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the time- frame in which the data associated with the instances was created.
January 2023 to June 2023 was the timeframe of data collection. The creation time of the songs is diverse and can be seen in Figure
6. Were any ethical review processes conducted (c.g., by an institutional review board)? If please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.
No.
7. Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)?
We collected data from Spotify and YouTube, not from artists directly.
8. Were the individuals in question notified about the data collection? If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself.
We did not notify any individuals about the data collection.
9. Did the individuals in question consent to the collection and use of their data? If so, please describe (or show with screenshots or other information) how consent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented.
We link to publicly available music on Spotify and YouTube. We allow every artist contained in our dataset to have their entries removed upon request.
10. If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses so, please provide a description, well as a link or other access point to the mechanism (if appropriate).
18

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Artists have the possibility to search our dataset for their YouTube video links, and their Spotify artist IDs and track IDs. If artists wish to remove their content from YouTuub or Spotify, they can contact those parties remove it themselves; this would result in our links becoming invalid. Additionally, we allow artists to contact us at disco.dataset ethz.ch to request the removal of their datapoints.
11.
Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., data protection impact analysis) been conducted?1f so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation.
Yes, discuss the implications of our data collection pipeline and of our dataset in Appendix[[
12. Any other comments? No.
J.4
Preprocsiingcleningllaiingg
Was any prepowesingcdeeninn/beelii the data done (e-g. discretization or bucket- ing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)?
We performed preprocessing by filtering, as described in Section 3.2 We do not process videos that are marked as age-restricted by YouTuue, and we provide the explicit content flag from Spotify.
2. Was the "raw" data saved in addition to the proproessaa/ctaaec/habee data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the "raw" data.
No.
3. Is the software that was used to preprocees/cden//lbbll the data available? If so, please provide a link or other access point.
We use spotipy access the Spotify API, youtubesearchpythoon query the YouTube search, and pytube to access the video on YouTube.
4. Any other comments?
No.
J.5
Uses
1. Has the dataset been used for any tasks already? If so, please provide a description.
No.
2. Is there a repository that links any or all papers or systems that use the dataset? so, please provide link or other access point.
No.
3. What (other) tasks could the dataset be used for?
We encourage the research community to use the dataset for music analysis, video anal- ysis, music information retrieval, generative models for music, music genre recognition, as well other possible downstream tasks enabled by the provided embeddings.
4. Is there anything about the composition of the dataset or the way it collected and prepocessed/clenee/abelee that might impact future uses? For example, is there anything that a dataset consumer might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other risks harms (c.g.. legal risks, financial harms)? If so, please provide a description. Is there anything a dataset consumer could do to mitigate these risks or harms?
Yes, as discussed Section
5. Are there tasks for which the dataset should not be used? If so, please provide description.
19

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We strongly advise to use DISCO-10M only for research purposes and not for commer- cial applications.
6. Any other comments?
No.
J.6 Distribution
Will the dataset be distributed to third parties outside of the entity (e.g,, company, institution, organization) behalf of which the dataset was created?lf so, please provide a description.
Yes, the dataset will be open-source.
2. How will the dataset will be distributed (e.g., tarball on website, API, GitHub)?Does the dataset have a digital object identifier (DOI)?
The dataset will be available on Hugging Face Datasets, DOI: 10.57967/h/00754
3. When will the dataset be distributed?
Starting from 14.06.2023.
4. Will the dataset distributed under copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, well any fees associated with these restrictions.
CC-BY-4.0
5.
Have any third parties imposed IP-based or other restrictions on the data associated with the instances?lf so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions.
We do own the copyright of the music accessible through the provided links.
6. Do any export controls other regulatory restrictions apply to the dataset or to individual instances? so, please describe these restrictions, and provide a link or other access point to, otherwise reproduce, any supporting documentation.
No.
7. Any other comments? No.
J.7
Maintenance
1. Who will be supportinghootingnnaaatinninn the dataset?
Hugging Face Datasets will host the dataset and we will maintain the dataset.
2. How can the ovner/curatorrmaaager of the dataset be contacted (e.g,, email address)?
The authors can be contacted via @ ethz.ch.
3. Is there an erratum?1f so, please provide a link or other access point.
Not initially, will started when necessary, and will be documented with future releases.
4. Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete in- stances)? so, please describe how often, by whom, and how updates will be communicated to dataset consumers (e.g., mailing list, GitHub)?
No, except for updates due to removal of dataset entries. Updates will be communicated on Hugging Face Datasets.
5. If the dataset relates to people, are there applicable limits on the retention of data associated with the instances (e.g,, were the individuals in question told that their data would be retained for fixed period time and then deleted)? If so, please describe these limits and explain how they will be enforced.
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Artists may contact us to have entries excluded from our dataset.
6. Will older versions of the dataset continue to be please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers.
If so,
There are no older versions of DISCO-10M.
7. If others want extend/aagmmettbuild on/contribute to the dataset, is there mech- anism for them to do so? If so, please provide a description. Will these contributions validated/verifiedd? If so, please describe how. If not, why not? Is there a process for communicatingddisriittnng these contributions to dataset consumers? If so, please provide description.
Updating and extending the dataset will be done case-by-case basis.
8. Any other comments?
No.
21


Reviews:
[{"review_id": "GON1c9owb6", "reviewer": "Reviewer_28sH", "summary": "", "questions": "", "limitations": "1. Other important meta information such as the genre and lyrics is missing, and such information can be also important for music information retrieval and music generation.\n\n2. The total duration of the dataset is unknown.\n\n\n\n", "rating": 7, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "The paper designs a pipeline to jointly use the information from Spotify and Youtube, and proposes several similarity measures to filter the audios.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "y63lnI6suA", "reviewer": "Reviewer_fHP3", "summary": "", "questions": "", "limitations": "- the author properly addressed the issue that the links might be expired in future, which highlight the drawback of not having audio signals in the dataset. However, I believe this will not impact the total amount for some years to come  \n", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "- the number of songs in the dataset is unprecedented scale\n- the curation scheme sounds straightforward and easy to reproduce\n- CLAP embeddings of songs in the dataset would save time and computes of people who use this dataset\n- similar songs in the dataset are taken care of by the filtering scheme, which ensures the diversity and quantity of dataset ", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "OWP42pcDgC", "reviewer": "Reviewer_LrKY", "summary": "", "questions": "", "limitations": "Yes", "rating": 7, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. This paper's primary and significant contribution lies in providing a new, extensive music dataset. The domain of music research has long struggled with a scarcity of publicly available large-scale data, hindering progress and limiting the scope of investigations. By presenting DISCO-10M, the paper empowers researchers with a valuable resource to explore scalable music informatics research.\n\n2. Another strength of this work is its clarity. The paper is well-motivated and easy to follow. All the data creation processes are clearly described, which enables reproducible research. Also, the paper explicitly addresses the limitations and potential ethical issues of the proposed dataset.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "XWMk1qnz2h", "reviewer": "Reviewer_pF6S", "summary": "", "questions": "", "limitations": "Limitations regarding the data that depend on external resources (Spotify and YouTube) are well acknowledged. The limitations related to the data collection itself or analysis of the obtained data have not been discussed much.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "The paper proposes a large-scale dataset which is expected to cover diversity of popular artists. The methods for forming the set of artists and songs and matching them with YouTube videos seem appropriately chosen. Overall this can be important contribution to the music information retrieval research community. \n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: This paper presents DISCO-10M, a large-scale music dataset to help advance the development of novel machine learning models for music. The dataset was collected through a careful filtering pipeline of curated music from Spotify and YouTube music. The authors also provide CLAP embeddings for DISCO-10M, allowing efficient exploration of machine learning applications. The reviewers agree about the usefulness of the dataset, but raise a concern about copyright issue of music. The authors highlight that they do not own the copyright of any music referenced in the dataset, thus only provide url links to the publicly available sources. In a positive view, this allows dataset users themseleves to assess the adequateness of DISCO-10M for research. The paper also provides a reference pipeline for them to collect other copyright-free music from the Internet.
Decision: Accept (Poster)