Classification of Guitar Effects and Extraction of Their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks

Hinrichs, Reemt; Gerkens, Kevin; Östermann, Jörn

doi:10.1007/978-3-031-03789-4_7

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…This manuscript expands our results of [14] in several ways: (i) multi-effects are also considered, i.e., sequences of guitar effects applied one after the other; (ii) results are expanded to four additional guitar effects, namely chorus, reverb, overdrive, and phaser, proving the versatility of the CNN; and (iii) the error analysis is more indepth.…”

Section: Contributionmentioning

confidence: 82%

“…Comunità et al [13] used convolutional neural networks (CNNs) to extract the parameter settings of different implementations of distortion-related guitar effects from monophonic and polyphonic audio samples, achieving below 0.1 root-mean-square error in all cases. In [14], a CNN was used for the classification of guitar effects from instrument mixes as well as the extraction of their parameter settings. The CNN was used for the extraction of single guitar effects limited to distortion, tremolo, and slapback delay.…”

Section: Introductionmentioning

confidence: 99%

“…The CNN yielded presumed human expert-level results for all effects considered. Except for [14], none of these research papers considered extraction of guitar effect parameter settings from instrument mixes, i.e., audio recordings or signals, in which several instruments play at once.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional neural networks for the classification of guitar effects and extraction of the parameter settings of single and multi-guitar effects from instrument mixes

Hinrichs

Gerkens

Lange

et al. 2022

J AUDIO SPEECH MUSIC PROC.

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Guitar effects are commonly used in popular music to shape the guitar sound to fit specific genres, or to create more variety within musical compositions. The sound not only is determined by the choice of the guitar effect, but also heavily depends on the parameter settings of the effect. Previous research focused on the classification of guitar effects and extraction of their parameter settings from solo guitar audio recordings. However, more realistic is the classification and extraction from instrument mixes. This work investigates the use of convolution neural networks (CNNs) for the classification and parameter extraction of guitar effects from audio samples containing guitar, bass, keyboard, and drums. The CNN was compared to baseline methods previously proposed, like support vector machines and shallow neural networks together with predesigned features. On two datasets, the CNN achieved classification accuracies $$1-5\,\%$$ 1 - 5 % above the baseline accuracy, achieving up to $$97.4\, \%$$ 97.4 % accuracy. With parameter values between 0.0 and 1.0, mean absolute parameter extraction errors of below 0.016 for the distortion, below 0.052 for the tremolo, and below 0.038 for the slapback delay effect were achieved, matching or surpassing the presumed human expert error of 0.05. The CNN approach was found to generalize to further effects, achieving mean absolute parameter extraction errors below 0.05 for the chorus, phaser, reverb, and overdrive effect. For sequentially applied combinations of distortion, tremolo, and slapback delay, the mean extraction error slightly increased from the performance for the single effects to the range of 0.05 to 0.1. The CNN was found to be moderately robust to noise and pitch changes of the background instrumentation suggesting that the CNN extracted meaningful features.

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Section: Contributionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

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Convolutional neural networks for the classification of guitar effects and extraction of the parameter settings of single and multi-guitar effects from instrument mixes

Hinrichs

Gerkens

Lange

et al. 2022

J AUDIO SPEECH MUSIC PROC.

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“…In the case of BE-AFX, neural networks are usually trained to minimize a loss function that aims at reconstructing the AFX chain and its parameters as did Hinrichs et al [10] or Lee et al [11]. However, while their approach is flexible, its training requires the knowledge of the used AFXs and their parameters.…”

Section: Related Workmentioning

confidence: 99%

Blind Estimation of Audio Effects Using an Auto-Encoder Approach and Differentiable Digital Signal Processing

Peladeau,

Peeters

2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Blind Estimation of Audio Effects (BE-AFX) aims at estimating the audio effects (AFXs) applied to an original, unprocessed audio sample solely based on the processed audio sample. To train such a system traditional approaches optimize a loss between ground truth and estimated AFX parameters. This involves knowing the exact implementation of the AFXs used for the process. In this work, we propose an alternative solution that eliminates the requirement for knowing this implementation. Instead, we introduce an auto-encoder approach, which optimizes an audio quality metric. We explore, suggest, and compare various implementations of commonly used mastering AFXs, using differential signal processing or neural approximations. Our findings demonstrate that our auto-encoder approach yields superior estimates of the audio quality produced by a chain of AFXs, compared to the traditional parameter-based approach, even if the latter provides a more accurate parameter estimation.

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Application of Neural Architecture Search to Instrument Recognition in Polyphonic Audio

Fricke¹,

Vatolkin²,

Ostermann³

2023

Lecture Notes in Computer Science

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Classification of Guitar Effects and Extraction of Their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks

Cited by 4 publications

References 6 publications

Convolutional neural networks for the classification of guitar effects and extraction of the parameter settings of single and multi-guitar effects from instrument mixes

Convolutional neural networks for the classification of guitar effects and extraction of the parameter settings of single and multi-guitar effects from instrument mixes

Blind Estimation of Audio Effects Using an Auto-Encoder Approach and Differentiable Digital Signal Processing

Application of Neural Architecture Search to Instrument Recognition in Polyphonic Audio

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