Music Information Retrieval

The Free Music Archive (FMA) is a large-scale dataset for evaluating several tasks in Music Information Retrieval. It consists of 343 days of audio from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a hierarchical taxonomy of 161 genres. It provides full-length and high-quality audio, pre-computed features, together with track- and user-level metadata, tags, and free-form text such as biographies. There are four subsets defined by the authors: * Full: the complete dataset, * Large: the full dataset with audio limited to 30 seconds clips extracted from the middle of the tracks (or entire track if shorter than 30 seconds), * Medium: a selection of 25,000 30s clips having a single root genre, * Small: a balanced subset containing 8,000 30s clips with 1,000 clips per one of 8 root genres. The official split into training, validation and test sets (80/10/10) uses stratified sampling to preserve the percentage of tracks per genre. Songs of the same artists are part of one set only. Source: FMA: A Dataset For Music Analysis Audio Source: https://github.com/mdeff/fma

MedleyDB, is a dataset of annotated, royalty-free multitrack recordings. It was curated primarily to support research on melody extraction. For each song melody f₀ annotations are provided as well as instrument activations for evaluating automatic instrument recognition. The original dataset consists of 122 multitrack songs out of which 108 include melody annotations. The songs in MedleyDB were obtained from the following sources: * Independent Artists (30 songs) * NYU's Dolan Recording Studio (32 songs) * Weathervane Music (25 songs) * Music Delta (35 songs) MedleyDB contains songs of a variety of musical genres: Singer/Songwriter, Classical, Rock, World/Folk, Fusion, Jazz, Pop, Musical Theatre, Rap. For each song three types of audio content are given: a mix, stems, and raw audio. All types of audio files are .wav files with a sample rate of 44.1 kHz and a bit depth of 16. Source: https://medleydb.weebly.com/ Image Source: https://medleydb.weebly.com/ Audio Source: https://zenodo.org/record/1438309

The RWC (Real World Computing) Music Database is a copyright-cleared music database (DB) that is available to researchers as a common foundation for research. It contains around 100 complete songs with manually labeled section boundaries. For the 50 instruments, individual sounds at half-tone intervals were captured with several variations of playing styles, dynamics, instrument manufacturers and musicians. Source: https://staff.aist.go.jp/m.goto/RWC-MDB/ Image Source: http://www.cs.tut.fi/sgn/arg/matti/demos/basstrans/

The MTG-Jamendo dataset is an open dataset for music auto-tagging. The dataset contains over 55,000 full audio tracks with 195 tags categories (87 genre tags, 40 instrument tags, and 56 mood/theme tags). It is built using music available at Jamendo under Creative Commons licenses and tags provided by content uploaders. All audio is distributed in 320kbps MP3 format. A subset of the dataset is used in the Emotion and Theme Recognition in Music Task within MediaEval 2019. Source: https://mtg.github.io/mtg-jamendo-dataset/ Audio Source: https://essentia.upf.edu/datasets/mtg-jamendo/raw_30s/audio/

URMP (University of Rochester Multi-Modal Musical Performance) is a dataset for facilitating audio-visual analysis of musical performances. The dataset comprises 44 simple multi-instrument musical pieces assembled from coordinated but separately recorded performances of individual tracks. For each piece the dataset provided the musical score in MIDI format, the high-quality individual instrument audio recordings and the videos of the assembled pieces. Source: http://www2.ece.rochester.edu/projects/air/projects/URMP.html Image Source: http://www2.ece.rochester.edu/projects/air/projects/URMP.html Audio Source: http://www2.ece.rochester.edu/projects/air/projects/URMP.html

GuitarSet is a dataset of high-quality guitar recordings and rich annotations. It contains 360 excerpts 30 seconds in length. The 360 excerpts are the result of the following combinations: * 6 players, * 2 versions: comping and soloing, * 5 styles: Rock, Singer-Songwriter, Bossa Nova, Jazz, and Funk, * 3 progressions: 12 Bar Blues, Autumn Leaves, and Pachelbel Canon, * 2 tempi: slow and fast. Each excerpt is annotated with 6 pitch contour and midi note annotations (one per string), 2 chord annotations (instructed and performed), beat and tempo annotations. Source: https://guitarset.weebly.com/ Audio Source: https://zenodo.org/record/3371780

EMOPIA (pronounced ‘yee-mò-pi-uh’) dataset is a shared multi-modal (audio and MIDI) database focusing on perceived emotion in pop piano music, to facilitate research on various tasks related to music emotion. The dataset contains 1,087 music clips from 387 songs and clip-level emotion labels annotated by four dedicated annotators.

The iKala dataset is a singing voice separation dataset that comprises of 252 30-second excerpts sampled from 206 iKala songs (plus 100 hidden excerpts reserved for MIREX data mining contest). The music accompaniment and the singing voice are recorded at the left and right channels respectively. Additionally, the human-labeled pitch contours and timestamped lyrics are provided. This dataset is not available anymore. Source: http://mac.citi.sinica.edu.tw/ikala/ 1. "T" as a sofa: The "T" horizontal strip can mimic the back of a sofa with a delicate cushion or details of the uphols or appliances with the color button. The "T" vertical strip can show a feet or arm of the sofa, shiny, yet firm. 2. Merge "P": Put "P" next to "T", your curve to delicately with the top "T." It is intertwined. The circular part of "P" can show a cushion or a curved chair and synchronize the subject of furniture. Make sure "P" is visually relying on "T", which reflects the relationship of cohesion and balance. 5. Coherence of "B" and "I": "B" can be aligned as a pair of cushions or a modern chair, with mild curves with glossy and modern aesthetics. "I" can be a symbol of a shiny furniture or a vertical light bar and completes the shapes without overburdess them. Color palette 4: Includes soft soil colors such as beige, top and gray shades, along with silent or silver gold tips to touch elegance. Consider a slope effect to enhance modernity, to keep colors elegant and complex. 5. Connect the letters: Use the overlap or intertwined edges that the letters meet for the symbol of unity. The plan should allow viewers to distinguish each letter while feeling part of the same "structure". 6. Background patterns: Use delicate geometric patterns or textures that mimic fabrics or furniture materials such as wood seeds or woven fibers. These patterns must remain minimalist and focus on highlighting the logo, while maintaining communication. While it deals with the subject of furniture and design, this concept conveys modernity, creativity and professional. If you like, I can create a draft design for better visualization.

The Lakh Pianoroll Dataset (LPD) is a collection of 174,154 multitrack pianorolls derived from the Lakh MIDI Dataset (LMD). ## Getting the dataset We provide multiple subsets and versions of the dataset (see here). The dataset is available here. ## Using LPD The multitrack pianorolls in LPD are stored in a special format for efficient I/O and to save space. We recommend to load the data with Pypianoroll (The dataset is created using Pypianoroll v0.3.0.). See here to learn how the data is stored and how to load the data properly. ## License Lakh Pianoroll Dataset is a derivative of Lakh MIDI Dataset by Colin Raffel, used under CC BY 4.0. Lakh Pianoroll Dataset is licensed under CC BY 4.0 by Hao-Wen Dong and Wen-Yi Hsiao. Please cite the following papers if you use Lakh Pianoroll Dataset in a published work. - Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, and Yi-Hsuan Yang, "MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment," in Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018. - Colin Raffel, "Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching," PhD Thesis, 2016. ## Related projects - MuseGAN - LeadSheetGAN

GiantMIDI-Piano contains 10,854 unique piano solo pieces composed by 2,786 composers. GiantMIDI-Piano contains 34,504,873 transcribed notes, and contains metadata information of each music piece. Source: GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music

OpenMIC-2018 is an instrument recognition dataset containing 20,000 examples of Creative Commons-licensed music available on the Free Music Archive. Each example is a 10-second excerpt which has been partially labeled for the presence or absence of 20 instrument classes by annotators on a crowd-sourcing platform. Source: OpenMIC-2018: An Open Data-set for Multiple Instrument Recognition Image Source: OpenMIC-2018: An Open Data-set for Multiple Instrument Recognition Audio Source: https://zenodo.org/record/1432913

The YouTube-100M data set consists of 100 million YouTube videos: 70M training videos, 10M evaluation videos, and 20M validation videos. Videos average 4.6 minutes each for a total of 5.4M training hours. Each of these videos is labeled with 1 or more topic identifiers from a set of 30,871 labels. There are an average of around 5 labels per video. The labels are assigned automatically based on a combination of metadata (title, description, comments, etc.), context, and image content for each video. The labels apply to the entire video and range from very generic (e.g. “Song”) to very specific (e.g. “Cormorant”). Being machine generated, the labels are not 100% accurate and of the 30K labels, some are clearly acoustically relevant (“Trumpet”) and others are less so (“Web Page”). Videos often bear annotations with multiple degrees of specificity. For example, videos labeled with “Trumpet” are often labeled “Entertainment” as well, although no hierarchy is enforced. Source: https://arxiv.org/pdf/1609.09430.pdf Image Source: https://arxiv.org/pdf/1609.09430.pdf

The Spotify Music Streaming Sessions Dataset (MSSD) consists of 160 million streaming sessions with associated user interactions, audio features and metadata describing the tracks streamed during the sessions, and snapshots of the playlists listened to during the sessions. This dataset enables research on important problems including how to model user listening and interaction behaviour in streaming, as well as Music Information Retrieval (MIR), and session-based sequential recommendations.

GoodSounds dataset contains around 28 hours of recordings of single notes and scales played by 15 different professional musicians, all of them holding a music degree and having some expertise in teaching. 12 different instruments (flute, cello, clarinet, trumpet, violin, alto sax alto, tenor sax, baritone sax, soprano sax, oboe, piccolo and bass) were recorded using one or up to 4 different microphones. For all the instruments the whole set of playable semitones in the instrument is recorded several times with different tonal characteristics. Each note is recorded into a separate monophonic audio file of 48kHz and 32 bits. Rich annotations of the recordings are available, including details on recording environment and rating on tonal qualities of the sound (“good-sound”, “bad”, “scale-good”, “scale-bad”). Source: A real-time system for measuring sound goodness in instrumental sounds Image Source: A real-time system for measuring sound goodness in instrumental sounds Audio Source: https://zenodo.org/record/820937

jazznet is a dataset of piano patterns for music audio machine learning research. The dataset comprises chords, arpeggios, scales, and chord progressions in all keys of an 88-key piano and in all the inversions, for a total of 162520 labeled piano patterns, resulting in 95GB of data and more than 26k hours of audio. The data is also accompanied by Python scripts to enable the easy generation of new piano patterns beyond those present in the dataset. The data is broken down into small, medium, and large subsets, comprising 21516, 30328, and 52360 patterns, respectively (with all the chords, arpeggios, and scales being present in all subsets). Source: jazznet: A Dataset of Fundamental Piano Patterns for Music Audio Machine Learning Research Image Source: https://github.com/tosiron/jazznet

MuMu is a new dataset of more than 31k albums classified into 250 genre classes. Source: Multi-label Music Genre Classification from Audio, Text, and Images Using Deep Features

COSIAN is an annotation collection of Japanese popular (J-POP) songs, focusing on singing style and expression of famous solo-singers. It consists of various 168 songs. There are 21 female- and 21 male singers. Each singer has four songs that have different moods from each other. Source: Analysis and Detection of Singing Techniques in Repertoires of J-POP Solo Singers Image Source: https://yamathcy.github.io/ISMIR2022J-POP/

This dataset includes all music sources, background noises and impulse-reponses (IR) samples and conversation speech that have been used in the work "Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning" ICASSP 2021 (https://arxiv.org/abs/2010.11910).

The CAL10K dataset (introduced as Swat10k) contains 10,870 songs that are weakly-labelled using a tag vocabulary of 475 acoustic tags and 153 genre tags. The tags have all been harvested from Pandora’s website and result from song annotations performed by expert musicologists involved with the Music Genome Project. Source: Exploring automatic music annotation with “acoustically-objectiv” tags

Guitar-TECHS is a comprehensive dataset featuring a variety of guitar techniques, musical excerpts, chords, and scales. These elements are performed by diverse musicians across various recording settings. Guitar-TECHS incorporates recordings from two stereo microphones: an egocentric microphone positioned on the performer’s head and an exocentric microphone placed in front of the performer. It also includes direct input recordings and microphoned amplifier outputs, offering a wide spectrum of audio inputs and recording qualities. All signals and MIDI labels are properly synchronized. Its multi-perspective and multi-modal content makes Guitar-TECHS a valuable resource for advancing data-driven guitar research, and to develop robust guitar listening algorithms.

The Haydn Annotation Dataset consists of note onset annotations from 24 experiment participants with varying musical experience. The annotation experiments use recordings from the ARME Virtuoso Strings Dataset. Source: Annotation of Soft Onsets in String Ensemble Recordings Image Source: https://arxiv.org/pdf/2211.08848v1.pdf

Introduction The Niko Chord Progression Dataset is used in AccoMontage2. It contains 5k+ chord progression pieces, labeled with styles. There are four styles in total: Pop Standard, Pop Complex, Dark and R&B. Some progressions have an 'Unknown' style. Some statistics are provided below. | | Mean | Variance | | -------------------------- | ----- | -------- | | Note Pitch | 57 | 167.70 | | Note Velocity | 79.05 | 457.89 | | Note Duration (in seconds) | 1.38 | 1.62 | ### Data Formats You can access the Niko Chord Progression Dataset in two formats: MIDI format and the quantized note matrix format. ##### MIDI (dataset.zip) Each chord progression piece is stored as a single MIDI file. ##### Quantized Note Matrix (dataset.pkl) A python dictionary with format like the following. nmatis an 2-d matrix, each row represent a quantized note: [start, end, pitch, velocity]. <u>Each note is quantized at the eighth note level. eg., start=2 means the note begins at the third eighth note.</u> root is also an 2-d matrix. It labels the roots of the chords using an eighth note sample rate. Each row of the root represents a bar. Each element is an integer ranged from 0 (C note) to 11 (B note). python {'piece name': {'nmat': [[0, 3, 60, 60], ...], # 2-d matrix: note matrix 'root': [[0,0,0,0,0,0,0,0], ...], # 2-d matrix: root label 'style': 'some style', # pop_standard, pop_complex, dark, r&b, unknown 'mode': 'some mode', # M, m 'tonic': 'some tonic' # C, Db, ... B }, ... } # load the dataset using pickle import pickle with open('dataset_path_and_name.pkl', 'rb') as file: dataset = pickle.load(file) ### Supplementary description ##### Original Dataset The Niko Chord Progression Dataset is a re-organized version of the original Niko Dataset. The original Niko Dataset have duplicate progressions and unnecessary labels, it was thus processed and converted to this version. ##### Style Mapping The style label was mapped from the original dataset to the new dataset. The style label in the original dataset is stored as folder names, and thus the style can be obtained from the file path. The following shows a detailed description of the style mapping function. // Structure of the original dataset . ├─A Major - F# Minor ---> progressions are sorted based on tonics and modes │ ├─1 - Best Melodies ---> eliminated │ │ ├─Catchy │ │ ├─Dark_HipHop_Trap │ │ ├─EDM │ │ ├─Emotional │ │ ├─Pop │ │ └─R&B_Neosoul │ ├─2 - Best Chords │ │ ├─Dark_HipHop_Trap ---> New style: Dark │ │ ├─EDM │ │ │ ├─Classy_7th_9th ---> New style: Pop Complex │ │ │ ├─Emotional ---> New style: Pop Complex │ │ │ └─Standard ---> New style: Pop Standard │ │ ├─Emotional ---> New style: Pop Complex │ │ ├─Pop │ │ │ ├─Classy_7th_9th ---> New style: Pop Complex │ │ │ ├─Emotional ---> New style: Pop Complex │ │ │ └─Standard ---> New style: Pop Standard │ │ └─R&B_Neosoul ---> New style: R&B │ └─3 - Rest Of Pack │ ├─A-Bm-D (I-ii-IV) ---> progressions sorted based on root pattern │ │ ├─Arps ---> eliminated │ │ ├─Basslines ---> eliminated │ │ ├─Chord Breakdown ---> New style: Unknown │ │ ├─Chord Progression -> New style: Unknown │ │ ├─Epic Endings ---> eliminated │ │ ├─Fast Chord Rhythm -> eliminated │ │ │ ├─Back & Forth │ │ │ └─Same Time │ │ ├─Melodies ---> eliminated │ │ │ ├─115-130bpm │ │ │ ├─130-160bpm │ │ │ ├─160-180bpm │ │ │ └─90-115bpm │ │ └─Slow Chord Rhythm -> New style: Unknown ... ### Cite L. Yi, H. Hu, J. Zhao, and G. Xia, “AccoMontage2: A Complete Harmonization and Accompaniment Arrangement System”, in Proceedings of the 23rd International Society for Music Information Retrieval Conference, Bengaluru, India, 2022. ### License MIT Licensed. Copyright © 2022 New York University Shanghai Music X Lab. All rights reserved.

Nlakh is a dataset for Musical Instrument Retrieval. It is a combination of the NSynth dataset, which provides a large number of instruments, and the Lakh dataset, which provides multi-track MIDI data. Source: Show Me the Instruments: Musical Instrument Retrieval from Mixture Audio Image Source: https://arxiv.org/pdf/2211.07951v1.pdf

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Virtuoso Strings is a dataset for soft onsets detection for string instruments. It consists of over 144 recordings of professional performances of an excerpt from Haydn's string quartet Op. 74 No. 1 Finale, each with corresponding individual instrumental onset annotations. Source: Annotation of Soft Onsets in String Ensemble Recordings Image Source: https://arxiv.org/pdf/2211.08848v1.pdf

This publicly available data is synthesised audio for woodwind quartets including renderings of each instrument in isolation. The data was created to be used as training data within Cadenza's second open machine learning challenge (CAD2) for the task on rebalancing classical music ensembles. The dataset is also intended for developing other music information retrieval (MIR) algorithms using machine learning. It was created because of the lack of large-scale datasets of classical woodwind music with separate audio for each instrument and permissive license for reuse. Music scores were selected from the OpenScore String Quartet corpus. These were rendered for two woodwind ensembles of (i) flute, oboe, clarinet and bassoon; and (ii) flute, oboe, alto saxophone and bassoon. This was done by a professional music producer using industry-standard software. Virtual instruments were used to create the audio for each instrument using software that interpreted expression markings in the score. Convolution reverberation was used to simulate a performance space and the ensembles mixed. The dataset consists of the audio and associated metadata Nineteen scores were randomly selected from the OpenScore String Quartet corpus. The synthesis of these as woodwind ensembles was performed by a sound engineering professional using professional software. The scores were loaded into the music notation software Steinberg's Dorico. The string parts were allocated to flute, oboe, clarinet (or alto saxophone) and bassoon. Virtual instruments were used to create the audio for each instrument: Miroslav Philharmonik 2 by IK Multimedia for the saxophone and Intimate Studio Winds by 8Dio for the other parts. The quartets were then imported into Avid's Pro Tools where they were mixed and reverberation added. Metadata were generated..