An Improved Measure of Musical Noise Based on Spectral Kurtosis

Over the past few decades, computational methods have been developed to estimate perceptual audio quality. These methods, also referred to as objective quality measures, are usually developed and intended for a specific application domain. Because of their convenience, they are often used outside their original intended domain, even if it is unclear whether they provide reliable quality estimates in this case. This work studies the correlation of well-known state-of-the-art objective measures with human perceptual scores in two different domains: audio coding and source separation. The following objective measures are considered: fwS-NRseg, dLLR, PESQ, PEAQ, POLQA, PEMO-Q, ViSQOLAudio, (SI-)BSSEval, PEASS, LKR-PI, 2f-model, and HAAQI. Additionally, a novel measure (SI-SA2f) is presented, based on the 2f-model and a BSSEval-based signal decomposition. We use perceptual scores from 7 listening tests about audio coding and 7 listening tests about source separation as ground-truth data for the correlation analysis. The results show that one method (2f-model) performs significantly better than the others on both domains and indicate that the dataset for training the method and a robust underlying auditory model are crucial factors towards a universal, domainindependent objective measure.

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“…LKR-PI [56] is a measure of perceived musical artifacts (a.k.a. artificial noise, musical noise, or birdies) caused by spectral holes or islands.…”

Section: K Log Kurtosis Ratio Perceptually Improved (Lkr-pi)mentioning

confidence: 99%

Objective Measures of Perceptual Audio Quality Reviewed: An Evaluation of Their Application Domain Dependence

Torcoli

Kastner

Herre

2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…Musical noise is a known artefact introduced by the frequency-domain TSM, likely caused be periodicity introduced to noise components of the signal due to the sum-ofsines model of the STFT. This results in holes and/or peaks in the power spectrum that are heard as musical noise (Torcoli, 2019) and was explored as a possible feature. Spectral kurtosis, as proposed by Torcoli (2019), was explored using all previously discussed methods of alignment.…”

Section: B Additional Featuresmentioning

confidence: 99%

“…This results in holes and/or peaks in the power spectrum that are heard as musical noise (Torcoli, 2019) and was explored as a possible feature. Spectral kurtosis, as proposed by Torcoli (2019), was explored using all previously discussed methods of alignment. Lower, middle, and upper frequency bands were used in addition to the maximum across all bands.…”

Section: B Additional Featuresmentioning

confidence: 99%

An objective measure of quality for time-scale modification of audio

Roberts

Paliwal

2021

The Journal of the Acoustical Society of America

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Objective evaluation of audio processed with time-scale modification (TSM) remains an open problem. Recently, a dataset of time-scaled audio with subjective quality labels was published and used to create an initial objective measure of quality (OMOQ). In this paper, an improved OMOQ for time-scaled audio is proposed. The measure uses handcrafted features and a fully connected network to predict subjective mean opinion scores (SMOS). Basic and advanced perceptual evaluation of audio quality features are used in addition to nine features specific to TSM artefacts. Six methods of alignment are explored with interpolation of the reference magnitude spectrum to the length of the test magnitude spectrum giving the best performance. The proposed measure achieves a mean root mean square error of 0.490 and a mean Pearson correlation of 0.864 to SMOS, equivalent to the 97th and 82nd percentiles of the subjective sessions, respectively. The proposed measure is used to evaluate TSM algorithms, finding that Elastique gives the highest objective quality for solo instrument and voice signals, whereas the identity phase-locking phase vocoder gives the highest objective quality for music signals and the best overall quality. The objective measure is available online at https://www.github.com/zygurt/TSM.

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“…Laroche and Dolson (1999) suggest that this is due to a change in relationship between the phases of bins in the spectral domain. Musical noise, also known as musical artefacts or musical tones, is due to isolated holes and/or peaks within the power spectrum (Torcoli, 2019). Within TSM, these artefacts are caused by periodicity introduced to noise bins during phase progression, due to the sum of sines model of the Short Time Fourier Transform (STFT).…”

Section: Introductionmentioning

confidence: 99%

A time-scale modification dataset with subjective quality labels

Roberts

Paliwal

2020

The Journal of the Acoustical Society of America

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Time Scale Modification (TSM) is a well-researched field; however, no effective objective measure of quality exists. This paper details the creation, subjective evaluation, and analysis of a dataset for use in the development of an objective measure of quality for TSM. Comprised of two parts, the training component contains 88 source files processed using six TSM methods at 10 time scales, while the testing component contains 20 source files processed using three additional methods at four time scales. The source material contains speech, solo harmonic and percussive instruments, sound effects, and a range of music genres. Ratings (42 529) were collected from 633 sessions using laboratory and remote collection methods. Analysis of results shows no correlation between age and quality of rating; expert and non-expert listeners to be equivalent; minor differences between participants with and without hearing issues; and minimal differences between testing modalities. A comparison of published objective measures and subjective scores shows the objective measures to be poor indicators of subjective quality. Initial results for a retrained objective measure of quality are presented with results approaching average root mean squared error loss and Pearson correlation values of subjective sessions. The labeled dataset is available at http://ieee-dataport.org/1987.

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An Improved Measure of Musical Noise Based on Spectral Kurtosis

Cited by 7 publications

References 10 publications

Objective Measures of Perceptual Audio Quality Reviewed: An Evaluation of Their Application Domain Dependence

Objective Measures of Perceptual Audio Quality Reviewed: An Evaluation of Their Application Domain Dependence

An objective measure of quality for time-scale modification of audio

A time-scale modification dataset with subjective quality labels

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