Automatic Music Transcription Research Articles

From Pitches to Notes: Creation and Segmentation of Pitch Tracks for Melody Detection in Polyphonic Audio

Despite the importance of the note as the basic representational symbol in Western music notation, the explicit and accurate recognition of musical notes has been a difficult problem in automatic music transcription research. In fact, most approaches disregard the importance of notes as musicological units having dynamic nature. In this paper we propose a mechanism for quantizing the temporal sequences of the detected fundamental frequencies into note symbols, characterized by precise temporal boundaries and note pitches (namely, MIDI note numbers). The developed method aims to cope with typical dynamics and performing styles such as vibrato, glissando or legato.

Application-Specific Music Transcription for Tutoring

Automatic music transcription (AMT) refers to the ability of computers to write note information, such as the pitch, onset time, duration, and source of each sound, after listening to the music. Our application scenario is computer-assisted, musical-instrument tutoring, where the user has to practice most of the time without human coaching. The target users of our initial system are beginning violin students and singing students who are comfortable with computers. After consulting with a professional violin teacher and collaborators from our University's Conservatory of Music, we tried the following three strategies to build a system that can be tested by users.

A method for multiple F0 detection of continuous audio with dissimilar timbres

This paper discusses a method for multiple fundamental frequency (F0) tracking performed on a polyphonic mixture of musical instruments with dissimilar timbres recorded on a single monaural track. For each time frame, given a number of possible F0 candidates, F0 trajectories are extracted using an HMM-like method where observations are the input spectra and each F0 candidate is represented by one of the hidden states. The F0 trajectories are optimized according to a harmonic overlap score for each F0 candidate and a continuity score that is based on a distance between the F0 candidate’s harmonic amplitudes and frequencies calculated for all possible candidate pairs at successive adjacent frames. This step leads to the formation of note tracks. In a second step, instrument identification is performed on harmonic spectra associated with these note tracks to group similar timbres together. The method is demonstrated with an acoustic audio recording of a woodwind quintet. Applications include audio-to-MIDI conversion, instrument/voice separation, and automatic music transcription. F0 tracks can also be used as high level features for music information retrieval systems.

Sparse representations of polyphonic music

We consider two approaches for sparse decomposition of polyphonic music: a time-domain approach based on a shift-invariant model, and a frequency-domain approach based on phase-invariant power spectra. When trained on an example of a MIDI-controlled acoustic piano recording, both methods produce dictionary vectors or sets of vectors which represent underlying notes, and produce component activations related to the original MIDI score. The time-domain method is more computationally expensive, but produces sample-accurate spike-like activations and can be used for a direct time-domain reconstruction. The spectral-domain method discards phase information, but is faster than the time-domain method and retains more higher-frequency harmonics. These results suggest that these two methods would provide a powerful yet complementary approach to automatic music transcription or object-based coding of musical audio.

Automatic Music Transcription Supporting Different Instruments

Automatic music recognition from an audio performance is a key challenge for coding music in Western music notation in the digital world. This problem has been addressed in several ways, obtaining suitable results when a single specific instrument and monophonic music are processed. The development of a system for automatic music transcription that is able to cope with different music instruments is the aim of this article. Experimental results have shown that for monophonic pieces, recognition is quite viable and the process can be parameterized to realize a music-independent recognition tool and process.

Polyphonic monotimbral music transcription using dynamic networks

The automatic extraction of the notes that were played in a digital musical signal (automatic music transcription) is an open problem. A number of techniques have been applied to solve it without concluding results. The monotimbral polyphonic version of the problem is posed here: a single instrument has been played and more than one note can sound at the same time. This work tries to approach it through the identification of the pattern of a given instrument in the frequency domain. This is achieved using time-delay neural networks that are fed with the band-grouped spectrogram of a polyphonic monotimbral music recording. The use of a learning scheme based on examples like neural networks permits our system to avoid the use of an auditory model to approach this problem. A number of issues have to be faced to have a robust and powerful system, but promising results using synthesized instruments are presented.

Automatic Music Transcription as We Know it Today

The aim of this overview is to describe methods for the automatic transcription of Western polyphonic music. The transcription task is here understood as transforming an acoustic musical signal into a MIDI-like symbolic representation. Only pitched musical instruments are considered: recognizing the sounds of drum instruments is not discussed. The main emphasis is laid on estimating the multiple fundamental frequencies of several concurrent sounds. Various approaches to solve this problem are discussed, including methods that are based on modelling the human auditory periphery, methods that mimic the human auditory scene analysis function, signal model-based Bayesian inference methods, and data-adaptive methods. Another subproblem addressed is the rhythmic parsing of acoustic musical signals. From the transcription point of view, this amounts to the temporal segmentation of music signals at different time scales. The relationship between the two subproblems and the general structure of the transcription problem is discussed.

Automatic Music Transcription and Audio Source Separation

In this article, we give an overview of a range of approaches to the analysis and separation of musical audio. In particular, we consider the problems of automatic music transcription and audio source separation, which are of particular interest to our group. Monophonic music transcription, where a single note is present at one time, can be tackled using an autocorrelation-based method. For polyphonic music transcription, with several notes at any time, other approaches can be used, such as a blackboard model or a multiple-cause/sparse coding method. The latter is based on ideas and methods related to independent component analysis (ICA), a method for sound source separation.

Rhythm Quantization for Transcription

Automatic Music Transcription is the extraction of an acceptable notation from performed music. One important task in this problem is rhythm quantization which refers to categorization of note durations. Although quantization of a pure mechanical performance is rather straightforward, the task becomes increasingly difficult in presence of musical expression, i.e. systematic variations in timing of notes and tempo changes. For quantization of natural performances, we employ a framework based on Bayesian statistics. We demonstrate that some simple quantization schemata can be derived in this framework by simple assumptions about timing deviations. A general quantization method, which can be derived in this framework, is vector quantization (VQ). The algorithm operates on short groups of onsets and is thus flexible in capturing the structure of timing deviations between neighboring onsets and thus performs better than simple rounding methods. Finally, we present some results on simple examples.

Method for automatically transcribing music and apparatus therefore

An automatic music transcription method and system for generating a muscial score from an input acoustic signal. The acoustic signal may include vocal songs, vocal humming, and music from musical instruments. The system comprises means for extracting pitch information and power information from the input acoustic signal, for correcting the pitch information based on the deviation of the acoustic signal relative to an absolute musical scale, for dividing the acoustic signal into a set of single-sound segments using the corrected pitch information, dividing the acoustic signal into a second set of single-sound segments this time using changes in the power information, for dividing the acoustic signal in still greater detail using information contained in both previous segmentations, for associating each segment with a musical interval of an absolute musical scale, and for determining single-sounds segments depending on whether or not the musical intervals of adjacent segments are identical, for determining the key of the acoustic signal, for correcting the placement of the segments on the musical scale of the determined key using the pitch information, for determining the time and tempo of the acoustic signal using this placement, and for compiling musical score data using the determined musical scale, sound length, key, time, and tempo of the acoustic signal.