Towards Blind Quality Assessment of Concert Audio Recordings Using Deep Neural Networks

Simou, Nikonas; Mastorakis, Yannis; Stefanakis, Nikolaos

doi:10.1109/icassp40776.2020.9053356

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…Other NR tools produce estimates of objective values including FR speech quality values [23], [30], [32], [38], [44], [51], [54], [56], [57], FR speech intelligibility values [30], [32], [38], [44], [52], [54], [56], [57], speech transmission index [22], codec bit-rate [46], and detection of specific impairments, artifacts, or noise types [34], [39], [41], [52]. Some of these tools perform a single task and others perform multiple tasks.…”

Section: A Existing Machine Learning Approachesmentioning

confidence: 99%

“…They address application areas that include room acoustics (noise and reverberation), speech enhancers, telecommunications systems, and hearing aids. Each addresses one or more of narrowband (NB) (nominally 300 Hz-3.5 kHz), wideband (WB) (nominally 50 Hz-7 kHz), super-wideband (SWB) (nominally 50 Hz-16 kHz), or fullband (FB) (nominally 20 Hz-20 kHz) speech, except for [34], [46] which address music. Recent work shows that NR-tools can measure the speech quality at a system input in spite of the fact that such tools can only access the system output.…”

Section: A Existing Machine Learning Approachesmentioning

confidence: 99%

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Wideband Audio Waveform Evaluation Networks: Efficient, Accurate Estimation of Speech Qualities

Catellier,

Voran

2023

IEEE Access

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Speech quality and speech intelligibility can vary dramatically across the wide range of currently available telecommunications systems, devices, and operating environments. This creates a strong demand for efficient real-time measurements of quality and intelligibly. Wideband Audio Waveform Evaluation Networks (WAWEnets) are convolutional neural networks that operate directly on wideband audio waveforms in order to produce evaluations of those waveforms. In the present work these evaluations give qualities of telecommunications speech (e.g., noisiness, intelligibility, overall speech quality). WAWEnets are no-reference networks because they do not require ''reference'' (original or undistorted) versions of the waveforms they evaluate. Our initial WAWEnet publication introduced four WAWEnets and each emulated the output of an established full-reference speech quality or intelligibility estimation algorithm. We have updated the WAWEnet architecture to be more efficient and effective. Here we present a single WAWEnet that closely tracks seven different quality and intelligibility values with per-segment correlations in the range of 0.92 to 0.96. We create a second network that additionally tracks four subjective speech quality dimensions. We offer a third network that focuses on just subjective quality scores and achieves a per-segment correlation of 0.97. The performance of our WAWEnet architecture compares favorably to models with orders-of-magnitude more parameters and computational complexity. This work has leveraged 334 hours of speech in 13 languages, over two million full-reference target values and over 93,000 subjective mean opinion scores. We also interpret the operation of WAWEnets and identify the key to their operation using the language of signal processing: ReLUs strategically move spectral information from non-DC components into the DC component. The DC values of 96 output signals define a vector in a 96-D latent space and this vector is then mapped to a quality or intelligibility value for the input waveform.

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Section: A Existing Machine Learning Approachesmentioning

confidence: 99%

Section: A Existing Machine Learning Approachesmentioning

confidence: 99%

Wideband Audio Waveform Evaluation Networks: Efficient, Accurate Estimation of Speech Qualities

Catellier,

Voran

2023

IEEE Access

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“…The non-intrusive speech quality assessment model called NISQA [50] produces estimates of subjective speech quality as well as four constituent dimensions: noisiness, coloration, discontinuity, and loudness. Other NR tools produce estimates of objective values including FR speech quality values [22], [29], [31], [42], [48], FR speech intelligibility values [29], [31], [42], [49], speech transmission index [21], codec bit-rate [43], and detection of specific impairments, artifacts, or noise types [33], [37], [39], [49]. Some of these tools perform a single task and others perform multiple tasks.…”

Section: A Existing Machine Learning Approachesmentioning

confidence: 99%

“…They address application areas that include room acoustics (noise and reverberation), speech enhancers, telecommunications systems, and hearing aids. Each addresses one or more of narrowband (NB) (nominally 300 Hz-3.5 kHz), wideband (WB) (nominally 50 Hz-7 kHz), super-wideband (SWB) (nominally 50 Hz-16 kHz), or fullband (FB) (nominally 20 Hz-20 kHz) speech, except for [33], [43] which address music. Recent work shows that NR-tools can measure the speech quality at a system input in spite of the fact that such tools can only access the system output.…”

Section: A Existing Machine Learning Approachesmentioning

confidence: 99%

Wideband Audio Waveform Evaluation Networks: Efficient, Accurate Estimation of Speech Qualities

Catellier¹,

Voran²

2022

Preprint

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Wideband Audio Waveform Evaluation Networks (WAWEnets) are convolutional neural networks that operate directly on wideband audio waveforms in order to produce evaluations of those waveforms. In the present work these evaluations give qualities of telecommunications speech (e.g., noisiness, intelligibility, overall speech quality). WAWEnets are no-reference networks because they do not require “reference” (original or undistorted) versions of the waveforms they evaluate. Our initial WAWEnet publication introduced four WAWEnets and each emulated the output of an established full-reference speech quality or intelligibility estimation algorithm. We have updated the WAWEnet architecture to be more efficient and effective. Here we present a single WAWEnet that closely tracks seven different quality and intelligibility values. We create a second network that additionally tracks four subjective speech quality dimensions. We offer a third network that focuses on just subjective quality scores and achieves very high levels of agreement. This work has leveraged 334 hours of speech in 13 languages, over two million full-reference target values and over 93,000 subjective mean opinion scores. We also interpret the operation of WAWEnets and identify the key to their operation using the language of signal processing: ReLUs strategically move spectral information from non-DC components into the DC component. The DC values of 96 output signals define a vector in a 96-D latent space and this vector is then mapped to a quality or intelligibility value for the input waveform.

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Telepresence Video Quality Assessment

Ying

Ghadiyaram

Bovik

2022

Lecture Notes in Computer Science

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Towards Blind Quality Assessment of Concert Audio Recordings Using Deep Neural Networks

Cited by 4 publications

References 12 publications

Wideband Audio Waveform Evaluation Networks: Efficient, Accurate Estimation of Speech Qualities

Wideband Audio Waveform Evaluation Networks: Efficient, Accurate Estimation of Speech Qualities

Wideband Audio Waveform Evaluation Networks: Efficient, Accurate Estimation of Speech Qualities

Telepresence Video Quality Assessment

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