Purpose: Create and share a MATLAB library that performs data augmentation algorithms for audio data. This study aims to help machine learning researchers to improve their models using the algorithms proposed by the authors. Design/methodology/approach: The authors structured our library into methods to augment raw audio data and spectrograms. In the paper, the authors describe the structure of the library and give a brief explanation of how every function works. The authors then perform experiments to show that the library is effective. Findings: The authors prove that the library is efficient using a competitive dataset. The authors try multiple data augmentation approaches proposed by them and show that they improve the performance. Orig...
Digital signal processing is being increasingly used for audio processing applications. Digital audi...
A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learnin...
Includes bibliographical references (p. 200-201) and index.Book fair 2012.x, 206 p.
Purpose: Create and share a MATLAB library that performs data augmentation algorithms for audio data...
Introduction to Audio Analysis serves as a standalone introduction to audio analysis, providing theo...
We present a toolbox for multi-stimulus perceptual evaluation of audio samples. Different from MUSHR...
In this paper we describe a Matlab application for real time measurements in auditory physiology. We...
This paper presents an overview of the main multi-modal speech enhancement methods reported to date....
Data augmentation has proven to be effective in training neural networks. Recently, a method called ...
A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learnin...
A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learnin...
A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learnin...
Dataset supporting 'Auditory inspired machine learning techniques can improve speech intelligibi...
Applied Speech and Audio Processing is a MATLAB-based, one-stop resource that blends speech and hear...
Supervised machine learning relies on the accessibility of large datasets of annotated data. This is...
Digital signal processing is being increasingly used for audio processing applications. Digital audi...
A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learnin...
Includes bibliographical references (p. 200-201) and index.Book fair 2012.x, 206 p.
Purpose: Create and share a MATLAB library that performs data augmentation algorithms for audio data...
Introduction to Audio Analysis serves as a standalone introduction to audio analysis, providing theo...
We present a toolbox for multi-stimulus perceptual evaluation of audio samples. Different from MUSHR...
In this paper we describe a Matlab application for real time measurements in auditory physiology. We...
This paper presents an overview of the main multi-modal speech enhancement methods reported to date....
Data augmentation has proven to be effective in training neural networks. Recently, a method called ...
A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learnin...
A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learnin...
A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learnin...
Dataset supporting 'Auditory inspired machine learning techniques can improve speech intelligibi...
Applied Speech and Audio Processing is a MATLAB-based, one-stop resource that blends speech and hear...
Supervised machine learning relies on the accessibility of large datasets of annotated data. This is...
Digital signal processing is being increasingly used for audio processing applications. Digital audi...
A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learnin...
Includes bibliographical references (p. 200-201) and index.Book fair 2012.x, 206 p.