Audio Spectrogram Representations For Processing With Convolutional Neural Networks

Abstract

One of the decisions that arise when designing a neural network for any application is how the data should be represented in order to be presented to, and possibly generated by, a neural network. For audio, the choice is less obvious than it seems to be for visual images, and a variety of representations have been used for different applications including the raw digitized sample stream, hand-crafted features, machine discovered features, MFCCs and variants that include deltas, and a variety of spectral representations. This paper reviews some of these representations and issues that arise, focusing particularly on spectrograms for generating audio using neural networks for style transfer.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keywyse2017audio

Related papers

• Nnaudio: An On-the-fly GPU Audio To Spectrogram Conversion Toolbox Using 1D Convolution Neural Networks (2019)13.70
• Audio Classification Of Low Feature Spectrograms Utilizing Convolutional Neural Networks (2024)5.84
• Neuralogram: A Deep Neural Network Based Representation For Audio Signals (2019)0.00
• The Impact Of Audio Input Representations On Neural Network Based Music Transcription (2020)8.82
• Spectral And Rhythm Features For Audio Classification With Deep Convolutional Neural Networks (2024)0.00
• Enhancing Unsupervised Audio Representation Learning Via Adversarial Sample Generation (2023)0.00
• Time Domain Neural Audio Style Transfer (2017)0.00
• Learning Robust Heterogeneous Signal Features From Parallel Neural Network For Audio Sentiment Analysis (2018)0.00