Musical Instrument Recognition in Polyphonic Audio Using Missing Feature Approach

Giannoulis, Dimitrios; Klapuri, Anssi

doi:10.1109/tasl.2013.2248720

Cited by 30 publications

(23 citation statements)

References 26 publications

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“…In [73], the authors introduced a note-estimation-free instrument recognition system that made use of a spectrogram-like representation (Instrogram). A series of approaches incorporate missing feature theory and aim to generate time-frequency masks that indicate spectrotemporal regions that belong only to a particular instrument which can then be classified more accurately since regions that are corrupted by noise or interference are kept out of the classification process [42,53]. Lastly, a third category includes systems that try to jointly separate and recognise the instruments of the mixture by employing parametric signal models and probabilistic inference [67,126] or by utilizing a mid-level representation of the signal and trying to model it as a sum of instrument-and pitch-specific active atoms [6,83].…”

Section: Other Transcription Subtasksmentioning

confidence: 99%

Automatic music transcription: challenges and future directions

et al. 2013

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract 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. Permanent repository link

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Section: Other Transcription Subtasksmentioning

confidence: 99%

Automatic music transcription: challenges and future directions

et al. 2013

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“…Giannoulis Dimitrios and Anssi Klapuri [4] suggested method using local spectral features and Missing-feature technique for musical instrument recognition in polyphonic audio signals. They recommended a mask estimation technique based on the assumption that the spectral envelopes of musical sounds tend to be slowly-varying as a function of log-frequency.…”

Section: Research Reviewmentioning

confidence: 99%

Indian Musical Instrument Recognition using Modified LPC Features

Sankaye¹,

Mehrotra²,

Tandon³

2015

IJCA

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“…Earlier work, such as Chétry [5] focuses on identifying instruments in isolated instrument recordings, whereas later work such as Giannoulis and Klapuri [10] handles mixed instruments in polyphonic audio.…”

Section: Related Workmentioning

confidence: 99%

Incremental Dataset Definition for Large Scale Musicological Research

Wolff

Tidhar

Benetos

et al. 2014

Proceedings of the 1st International Workshop on Digital Libraries for Musicology

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Conducting experiments on large scale musical datasets often requires the definition of a dataset as a first step in the analysis process. This is a classification task, but metadata providing the relevant information is not always available or reliable and manual annotation can be prohibitively expensive. In this study we aim to automate the annotation process using a machine learning approach for classification. We evaluate the effectiveness and the trade-off between accuracy and required number of annotated samples. We present an interactive incremental method based on active learning with uncertainty sampling. The music is represented by features extracted from audio and textual metadata and we evaluate logistic regression, support vector machines and Bayesian classification. Labelled training examples can be iteratively produced with a web-based interface, selecting the samples with lowest classification confidence in each iteration.We apply our method to address the problem of instrumentation identification, a particular case of dataset definition, which is a critical first step in a variety of experiments and potentially also plays a significant role in the curation of digital audio collections. We have used the CHARM dataset to evaluate the effectiveness of our method and focused on a particular case of instrumentation recognition, namely on the detection of piano solo pieces. We found that uncertainty sampling led to quick improvement of the classification, which converged after ca. 100 samples to values above 98%. In our test the textual metadata yield better results * Dan Tidhar is also a member of the Department of Music at City University London. † Edouard Dumon is also a member of ENSTA Paristech. than our audio features and results depend on the learning methods. The results show that effective training of a classifier is possible with our method which greatly reduces the effort of labelling where a residual error rate is acceptable.

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Musical Instrument Recognition in Polyphonic Audio Using Missing Feature Approach

Cited by 30 publications

References 26 publications

Automatic music transcription: challenges and future directions

Automatic music transcription: challenges and future directions

Indian Musical Instrument Recognition using Modified LPC Features

Incremental Dataset Definition for Large Scale Musicological Research

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