Time-frequency analysis of musical signals

Pielemeier, William J.; Wakefield, Gregory H.; Simoni, Mary

doi:10.1109/5.535242

Cited by 39 publications

(34 citation statements)

References 48 publications

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Section: Polyphonic Transcription 1: Blackboard Systemmentioning

confidence: 99%

“…These typically permit finer time resolution (and consequently coarser frequency resolution) at higher frequencies, where the normal FFT has the same resolution in time and frequency for all frequency bands, and may help to distinguish note onsets or other fast changes (Pielemeier et al 1996).…”

Section: <Insert Figure 1 Here>mentioning

confidence: 99%

“…Alternative time-frequency analysis techniques can be used, such as the 8 Multi-resolution Fourier transform (MFT), Wavelets, and "ear models" built to emulate the human auditory system. These typically permit finer time resolution (and consequently coarser frequency resolution) at higher frequencies, where the normal FFT has the same resolution in time and frequency for all frequency bands, and may help to distinguish note onsets or other fast changes (Pielemeier et al 1996).The blackboard system proper is a knowledge-based inference engine that can incorporate information from a variety of sources to produce hypotheses about (in our case) the notes present in the audio signal. The blackboard is a hypothesis database upon which initial input observations are first written.…”

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Automatic Music Transcription and Audio Source Separation

Plumbley

Abdallah

Bello³

et al. 2002

Cybernetics and Systems

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2 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 autocorrelationbased 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.Keywords: Automatic music transcription, blind source separation, ICA, sparse coding, computational auditory scene analysis 3 Over the last decade or so, and particularly since the publication of Bregman's seminal book on Auditory Scene Analysis (Bregmann 1990), there has been an increasing interest in the problem of Computational Auditory Scene Analysis (CASA): how to design computer-based models that can analyze an auditory scene. Imagine you are standing in a busy street among a crowd of people. You can hear traffic noise, footsteps of people nearby, the bleeping of a pedestrian crossing, your mobile phone ringing, and colleagues behind you having a conversation.Despite all these different sound sources, you have a pretty good idea of what is going on around you. It is more than just a mess of overlapping noise, and if you try hard you can concentrate on one of these sources if it is important to you (such as the conversation behind you).This has proved to be a very difficult problem. It requires both separation of many sound sources, and analysis of the content of these sources. However, a few authors have begun to tackle this problem in recent years, with some success (see e.g. Ellis 1996).One particular aspect of auditory scene analysis of interest to our group is automatic music transcription. Here, the sound sources are one or more instruments playing a piece of music, and we wish to analyze this to identify the instruments that are playing, and when and for how long each note is played. From this analysis we should then be able to produce a written musical score that shows notes and the duration of each on a written conventional music notation (for conventional western music, at least). In principle, this musical score could then be used to recreate the musical piece that was played.We are also interested in the ability to separate sound sources based on their different locations in an auditory scene.This capability, known as blind source separation (Bell & Sejnowski 1995), can be useful on its own if we wish to eliminate some sound sources and concentrate on others. It also offers the potential to be combined with automatic music transcription systems in the future, to improve transcription performance based on the location of instruments as well as the different sounds they make. Automatic music transcriptionAs we mentioned above...

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Section: Polyphonic Transcription 1: Blackboard Systemmentioning

confidence: 99%

Section: <Insert Figure 1 Here>mentioning

confidence: 99%

mentioning

confidence: 99%

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Automatic Music Transcription and Audio Source Separation

Plumbley

Abdallah

Bello³

et al. 2002

Cybernetics and Systems

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“…While the temporal evolution of the signal is characterized by the time-based features, the spectral features are extracted based on the short-time spectrum using time-frequency transformations such as short-time Fourier transform, constant-Q transform, and wavelets (Alm and Walker 2002;Pielemeier et al 1996). Apparently, representing musical signals with time-frequency features are effective due to the nature of musical signals where the frequency varies with time.…”

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confidence: 99%

“…In one representation, the emphasis is given to the extraction of instantaneous frequency (IF) (Boashash 1992) for analysis and re-synthesis purposes built upon sinusoidal modeling (Pielemeier et al 1996;Beauchamp 2007;Goodwin and Vetterli 1996;Kronland-Martinet 1988). The IF of a signal can be extracted using wavelet analysis (Delprat et al 1992) based on the time-frequency energy localization (see Sejdić et al 2009;Shafi et al 2009, for recent overviews).…”

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confidence: 99%

Wavelet ridges for musical instrument classification

2011

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The time-varying frequency structure of musical signals have been analyzed using wavelets by either extracting the instantaneous frequency of signals or building features from the energies of sub-band coefficients. We propose to benefit from a combination of these two approaches and use the time-frequency domain energy localization curves, called as wavelet ridges, in order to build features for classification of musical instrument sounds. We evaluated the representative capability of our feature in different musical instrument classification problems using support vector machine classifiers. The comparison with the features based on parameterizing the wavelet sub-band energies confirmed the effectiveness of the proposed feature.

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Improvement of frequency extraction accuracy of linear predictive analysis by a combination of autocorrelation and integral procedure for noise wave shape

Murakawa

Ishiyama

Yokota

et al. 2004

Electron. Comm. Jpn. Pt. I

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SUMMARYThe accurate and rapid identification or detection of the electrical noise sources causing noise malfunctions in communication equipment is an important issue in determining corrective measures. In this paper, we explain an improvement in the frequency extraction accuracy of linear predictive analysis for noise waveforms in order to identify the noise sources from noise waveforms such as the audible noise generated by communication equipment like telephones and noise currents penetrating into the communication equipment. In this study, we propose an improvement of the frequency extraction accuracy of linear predictive analysis by using correlation and integration. This method preserves specific frequency components of the original noise waveforms although correlation and integration are performed. In this paper, we divide the noise waveform into finite-length segments, correlate and integrate the time series data within a given segment, and formulate dynamic frequency analysis (time-frequency analysis) based on linear predictive analysis for the time series data in the postprocessing. We also verify the effectiveness of the proposed method by using known noise waveforms. The result of applying the proposed method to a noise waveform that convolved white noise with sine wave signals having known frequencies was clearly the ability to extract the frequencies of the sine waves included in the noise waveform even for a signal-to-noise ratio (SNR) of 0 dB with an error range of about 10 to 15%. This assumes that the frequencies of the sine waves included in the noise can be extracted even under the worst case SNR due to the suppression of white noise by the correlation and the emphasis of the low-frequency components by the integration. Thus, we show that the characteristic frequencies in the noise waveform can be accurately extracted by using linear predictive analysis combining correlation and integration, and present the remaining issues.

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Time-frequency analysis of musical signals

Cited by 39 publications

References 48 publications

Automatic Music Transcription and Audio Source Separation

Automatic Music Transcription and Audio Source Separation

Wavelet ridges for musical instrument classification

Improvement of frequency extraction accuracy of linear predictive analysis by a combination of autocorrelation and integral procedure for noise wave shape

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