Polyphonic music transcription using note onset and offset detection

In this paper, an approach for polyphonic music transcription based on joint multiple-F0 estimation and note onset/offset detection is proposed. For preprocessing, the resonator time-frequency image of the input music signal is extracted and noise suppression is performed. A pitch salience function is extracted for each frame along with tuning and inharmonicity parameters. For onset detection, late fusion is employed by combining a novel spectral flux-based feature which incorporates pitch tuning information and a novel salience function-based descriptor. For each segment defined by two onsets, an overlapping partial treatment procedure is used and a pitch set score function is proposed. A note offset detection procedure is also proposed using HMMs trained on MIDI data. The system was trained on piano chords and tested on classic and jazz recordings from the RWC database. Improved transcription results are reported compared to state-of-the-art approaches

Automatic transcription of polyphonic music exploiting temporal evolution

PhDAutomatic music transcription is the process of converting an audio recording into a symbolic representation using musical notation. It has numerous applications in music information retrieval, computational musicology, and the creation of interactive systems. Even for expert musicians, transcribing polyphonic pieces of music is not a trivial task, and while the problem of automatic pitch estimation for monophonic signals is considered to be solved, the creation of an automated system able to transcribe polyphonic music without setting restrictions on the degree of polyphony and the instrument type still remains open. In this thesis, research on automatic transcription is performed by explicitly incorporating information on the temporal evolution of sounds. First efforts address the problem by focusing on signal processing techniques and by proposing audio features utilising temporal characteristics. Techniques for note onset and offset detection are also utilised for improving transcription performance. Subsequent approaches propose transcription models based on shift-invariant probabilistic latent component analysis (SI-PLCA), modeling the temporal evolution of notes in a multiple-instrument case and supporting frequency modulations in produced notes. Datasets and annotations for transcription research have also been created during this work. Proposed systems have been privately as well as publicly evaluated within the Music Information Retrieval Evaluation eXchange (MIREX) framework. Proposed systems have been shown to outperform several state-of-the-art transcription approaches. Developed techniques have also been employed for other tasks related to music technology, such as for key modulation detection, temperament estimation, and automatic piano tutoring. Finally, proposed music transcription models have also been utilized in a wider context, namely for modeling acoustic scenes

Multiple-instrument polyphonic music transcription using a convolutive probabilistic model

(Abstract to follow

Acoustic event detection for multiple overlapping similar sources

Many current paradigms for acoustic event detection (AED) are not adapted to the organic variability of natural sounds, and/or they assume a limit on the number of simultaneous sources: often only one source, or one source of each type, may be active. These aspects are highly undesirable for applications such as bird population monitoring. We introduce a simple method modelling the onsets, durations and offsets of acoustic events to avoid intrinsic limits on polyphony or on inter-event temporal patterns. We evaluate the method in a case study with over 3000 zebra finch calls. In comparison against a HMM-based method we find it more accurate at recovering acoustic events, and more robust for estimating calling rates.Comment: Accepted for WASPAA 201

Automatic music transcription: challenges and future directions

Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects