The MAPS dataset is the most used benchmark dataset for automatic music transcription (AMT). We propose here an updated version of the ground truth, containing precise beat, time signature, and key signature annotations

19th International Society for Music Information Retrieval Conference Late-Breaking Demos Session

BENETOS, E

YCART, A

English

Queen Mary Research Online

A-MAPS: AUGMENTED MAPS DATASETWITH RHYTHM AND KEY ANNOTATIONSAdrien Ycart and Emmanouil BenetosCentre for Digital Music, Queen Mary University of LondonABSTRACTThe MAPS dataset is the most used benchmark datasetfor automatic music transcription (AMT). We propose herean updated version of the ground truth, containing precisebeat, time signature, and key signature annotations.1. INTRODUCTIONAutomatic music transcription is a canonical task in mu-sic information retrieval [1]. Roughly, music transcriptionis the task of extracting from an audio recording a mu-sic score in staff notation representing what was played.Much of the literature has focused on the simpler, inter-mediate goal of extracting a symbolic representation de-scribing what notes were played and when, usually in theform of a list of note events (MIDI file), leaving aside taskssuch as beat tracking, rhythm transcription, pitch spelling,etc. In what follows, we call the former complete musictranscription (CMT) and the latter automatic music tran-scription (AMT).The MIDI Aligned Piano Sounds (MAPS) dataset [2]has become a widely-used benchmark dataset for AMT. Itcontains around 18 hours of classical piano music, alongwith aligned MIDI transcriptions. However, the informa-tion it contains is limited to the pitch, onset and offset timesin seconds of the notes in the recording (velocity is also in-cluded, but rarely used).Recently, some systems have incorporated additionalmusical information into AMT system, such as meter [6].With the progress of AMT, CMT has also gained atten-tion [3]. To make evaluation and comparison with state-of-the-art AMT systems possible, we propose some aug-mented annotations for MAPS, that include in particularrhythm (meter, note values), key, as well as other generalinformation, all the while preserving the pitch, onset andoffset as they were in the original MAPS files. We makethese annotations available for further use 1 , in the form ofMIDI files.A. Ycart is supported by a QMUL EECS Research Studentship. E.Benetos is supported by a UK RAEng Research Fellowship (RF/128).1 http://c4dm.eecs.qmul.ac.uk/ycart/a-maps.htmlc© Adrien Ycart and Emmanouil Benetos. Licensed undera Creative Commons Attribution 4.0 International License (CC BY 4.0).Attribution: Adrien Ycart and Emmanouil Benetos. “A-MAPS: Aug-mented MAPS Dataset with Rhythm and Key Annotations”, 19th Interna-tional Society for Music Information Retrieval Conference, Paris, France,2018.2. THE MAPS DATASETThe MAPS dataset contains MIDI files of polyphonic pi-ano music, along with aligned audio renditions, generatedusing synthetic pianos and Disklavier acoustic pianos. Italso contains recordings of isolated notes and chords, thatwe do not consider here.The MAPS MIDI files used to render the audio weretaken from the Piano-Midi.de 2 (PM) database. The PMdatabase was made by manually editing the velocities andthe tempo curves of quantised MIDI files in order to givethem a natural interpretation. These MIDI files originallycontained much more information (tempo and note values,but also key, textual indications, sustain pedal...) which,unfortunately, wasn’t kept in MAPS. We aim to retrievethis additional information and include it in the MAPSground-truth MIDI files.3. METHODDirectly using the PM files as ground truth is not pos-sible, since this dataset is continuously updated by itscreator, meaning many files have been slightly modifiedsince the creation of MAPS. We thus resort to a sym-bolic MIDI-to-MIDI alignment method [4] to align pairsof files. From these alignments, we get a correspondencetable that tells us to which PM quantised point each MAPSnote is aligned. More precisely, we get a table T such thatT [i] = [t, q] where t is a time in seconds and q is a quan-tised time in quarter notes. We remove the duplicates, sothat each q is unique (when a single q value corresponds tomany t values, we keep the first t value). From this cor-respondance table, we get a tempo curve following closelythe MAPS files. Let bpm(t) be the tempo value is quar-ter notes per minute at time t. For each T [i] = [t1, q1],T [i+ 1] = [t2, q2] we have the formula:bpm(t) =q2 − q1t2 − t1× 60 for t ∈ [t1, t2[In some cases, the ordering is not preserved, i.e. q1 <q2 but t1 ≥ t2. In such cases, we delete one of the twoproblematic lines, keeping the one for which the quantisedtime was closest to a 16th note subdivision. When the twoquantised times are both on a 16th note subdivision, wekeep the one that results in the smallest tempo deviation.Adapting the tempo curve does not yield perfect results:some notes were modified in the PM files after the creation2 http://piano-midi.de/δmean δmaxmin med max min med max0.0 0.028 3.18 0 1 384Table 1. Evaluation of the beat annotations: minimum,median and maximum values in MIDI ticks of δmean andδmax across the datasetof MAPS, or displaced during the re-alignment process inthe making of MAPS. To get the exact notes from MAPS,we match the notes in the MAPS files with those in thetempo-aligned PM files using the mir eval toolbox [5].We then remove all the PM notes that are unmatched, andadd the MAPS notes that are unmatched. We add the miss-ing notes to the staff holding the PM note to which theywere aligned by the alignment algorithm [4]. When addinga MAPS note aligned to no PM note, we add it to the righthand staff if its MIDI pitch is above 60, to the left handstaff otherwise (this concerns 2.7% of the notes). Eventu-ally, all the correct notes are retrieved, and their onset andoffset values are within 5ms of the original.Because of this second step, some notes in the alignedfiles are not exactly quantised to beat subdivisions, al-though they were in the original PM files. To know howmuch, we compare the quantised versions of the originalPM files and aligned files. We match the notes of the twofiles with [4], and for each pair of notes, we compute thedeviation between their onsets in MIDI ticks (1 tick is a480th of a quarter note). Then, for each file, we computethe mean (δmean) and maximum (δmax) deviations. We re-port the minimum, median, and maximum values of δmeanand δmax across the dataset in Table 1. It has to be notedthat the large maximum values recorded are most likelydue to notes that were modified in later versions of the PMMIDI files.4. CONTENTSAll the annotations that were in the original PM files wereretrieved. They include:• Tempo curve• Time signature• Durations of notes in fraction of a quarter note (someof them are approximate)• Key signature (always written as the major relative)• Sustain pedal activation• Separate left and right hand staff• Text annotations from the score (tempo indications,coda...).Yet, they do not include all the information needed forCMT, mostly due to limitations of the MIDI format. In par-ticular, they do not include pitch spelling (C] is the sameas D[), rhythm spelling (a quarter note tied to an eighthnote is the same as a dotted quarter note), ornaments (trillsare written as a succession of notes, not as a separate sym-bol), dynamics information (only the velocity of individualnotes is available), or short key modulations (only changesin key signature are written).Some data from the MAPS dataset was lost throughoutthe process. Overall, we lost around 13 minutes of data(1.2% of total duration). The details of the lost data can befound on the website 3 .5. APPLICATIONSThese annotations can be useful in the development ofmusically-informed AMT systems (for instance, key- ormeter-dependent systems). They can be used directly asinput to the systems to test their performance in the idealcase, or to assess the performance of sub-modules of thesystem.They can also be used for other tasks than AMT, suchas beat tracking, and meter or key detection from audio.Although the note values given in the annotations are notalways exact, they can provide some useful estimates forrhythm quantisation. The textual annotations could also beused for structure analysis.Finally, even though some elements are missing to re-construct a full score, we hope that all the information con-tained in these annotations can help working towards CMT,by providing a big amount of annotated audio data and al-lowing comparison with previous AMT systems.6. REFERENCES[1] E. Benetos, S. Dixon, D. Giannoulis, H. Kirchhoff, andA. Klapuri. Automatic music transcription: challengesand future directions. Journal of Intelligent Informa-tion Systems, 41(3):407–434, 2013.[2] V. Emiya, R. Badeau, and B. David. Multipitch esti-mation of piano sounds using a new probabilistic spec-tral smoothness principle. IEEE Transactions on Au-dio, Speech, and Language Processing, 18(6):1643–1654, 2010.[3] E. Nakamura, E. Benetos, K. Yoshii, and S. Dixon. To-wards complete polyphonic music transcription: Inte-grating multi-pitch detection and rhythm quantization.In ICASSP, 2018.[4] E. Nakamura, K. Yoshii, and H. Katayose. Perfor-mance error detection and post-processing for fast andaccurate symbolic music alignment. In ISMIR, 2017.[5] C. Raffel, B. McFee, E. J. Humphrey, J. Salamon,O. Nieto, D. Liang, and D. P. W. Ellis. mir eval: Atransparent implementation of common MIR metrics.In ISMIR, 2014.[6] A. Ycart and E. Benetos. Polyphonic music sequencetransduction with meter-constrained LSTM networks.In ICASSP, 2018.3 http://c4dm.eecs.qmul.ac.uk/ycart/a-maps.html

A-MAPS: Augmented MAPS Dataset with Rhythm and Key Annotations

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A-MAPS: Augmented MAPS Dataset with Rhythm and Key Annotations

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