Onset detection using comb filters

Gainza, Mikel; Coyle, Eugene; Lawlor, Bob

doi:10.1109/aspaa.2005.1540220

Cited by 10 publications

(11 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most common form of preprocessing used for musical onset detection is sub-band or multi-band decomposition. Most of the studies using multi-band processing, approach the problem in a similar fashion: dividing the signal into sub-bands, estimating OSS for each sub-band, combining either at the OSS level or the onset decision level to achieve a final decision for the onsets [6]- [10]. It is reported that the robustness is improved by using such methodology [7].…”

mentioning

confidence: 99%

Three Dimensions of Pitched Instrument Onset Detection

Holzapfel

Stylianou

Gedik

et al. 2010

IEEE Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

Abstract-In this paper, we suggest a novel group delay based method for the onset detection of pitched instruments. It is proposed to approach the problem of onset detection by examining three dimensions separately: phase (i.e., group delay), magnitude and pitch. The evaluation of the suggested onset detectors for phase, pitch and magnitude is performed using a new publicly available and fully onset annotated database of monophonic recordings which is balanced in terms of included instruments and onset samples per instrument, while it contains different performance styles. Results show that the accuracy of onset detection depends on the type of instruments as well as on the style of performance. Combining the information contained in the three dimensions by means of a fusion at decision level leads to an improvement of onset detection by about 8% in terms of F-measure, compared to the best single dimension.

show abstract

mentioning

confidence: 99%

Three Dimensions of Pitched Instrument Onset Detection

Holzapfel

Stylianou

Gedik

et al. 2010

IEEE Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

show abstract

“…In contrast, the onset detection system presented in [8], denoted as ODCF, utilises the harmonicity changes of the signal by using a bank of FIR comb filters, which also have a harmonic type of magnitude response. The filterbank utilises 12 comb filters, which delays cover the 12 semitones of octave 4.…”

Section: Onset Detection Based On Comb Filters (Odcf)mentioning

confidence: 99%

“…FIR comb filters can be efficiently implemented in the time domain. Thus, the harmonicity measure of [8] applied to a signal x(n) by using a comb filter with delay D will be given by:…”

Section: Onset Detection Based On Comb Filters (Odcf)mentioning

confidence: 99%

“…A more general approach to detect different types of ornamentation is presented here. The model utilises a very accurate onset detector [8], which detects very close events that typically occur when ornamentation is utilised. By defining a set of rules that describe different types of ornamentation, the transcription of these ornaments is performed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automating Ornamentation Transcription

Gainza¹,

Coyle²

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

View full text Add to dashboard Cite

A novel technique for detecting single and multi-note ornaments is presented. The system detects audio segments by utilising an onset detector based on comb filters (ODCF), which is capable of detecting very close events. In addition, a novel method to remove spurious onsets due to offset events is introduced. The system utilises musical ornamentation theory to decide whether a sequence of audio segments correspond to an ornamentation musical structure. In order to evaluate the results, a database of signals produced by different players using the three different instruments has been utilised. The results represent a step forward towards fully automating ornamentation transcription.

show abstract

“…In [14], a comparison between the approach in [1] and the psychoacoustically motivated approaches in [12], [13] was performed. Finally, Gainza et al [15] employed FIR comb filters on a frame by frame basis combining the inharmonicity properties with the energy increases of the signal onset.…”

Section: Introductionmentioning

confidence: 99%

Auditory Spectrum-Based Pitched Instrument Onset Detection

Benetos

Stylianou

2010

IEEE Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-In this paper, a method for onset detection of music signals using auditory spectra is proposed. The auditory spectrogram provides a time-frequency representation that employs a sound processing model resembling the human auditory system. Recent work on onset detection employs DFT-based features describing spectral energy and phase differences, as well as pitchbased features. These features are often combined for maximizing detection performance. Here, the spectral flux and phase slope features are derived in the auditory framework and a novel fundamental frequency estimation algorithm based on auditory spectra is introduced. An onset detection algorithm is proposed, which processes and combines the aforementioned features at the decision level. Experiments are conducted on a dataset covering 11 pitched instrument types, consisting of 1829 onsets in total. Results indicate that auditory representations outperform various state-of-the-art approaches, with the onset detection algorithm reaching an F-measure of 82.6%. Permanent repository link

show abstract

Onset detection using comb filters

Cited by 10 publications

References 9 publications

Three Dimensions of Pitched Instrument Onset Detection

Three Dimensions of Pitched Instrument Onset Detection

Automating Ornamentation Transcription

Auditory Spectrum-Based Pitched Instrument Onset Detection

Contact Info

Product

Resources

About