Audio Spectral Enhancement: Leveraging Autoencoders for Low Latency Reconstruction of Long, Lossy Audio Sequences

• URL: http://arxiv.org/abs/2108.03703v1
• Date: Sun, 8 Aug 2021 18:06:21 GMT
• Title: Audio Spectral Enhancement: Leveraging Autoencoders for Low Latency Reconstruction of Long, Lossy Audio Sequences
• Authors: Darshan Deshpande and Harshavardhan Abichandani
• Abstract summary: We propose a novel approach for reconstructing higher frequencies from considerably longer sequences of low-quality MP3 audio waves. Our architecture presents several bottlenecks while preserving the spectral structure of the audio wave via skip-connections. We show how to leverage differential quantization techniques to reduce the initial model size by more than half while simultaneously reducing inference time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With active research in audio compression techniques yielding substantial breakthroughs, spectral reconstruction of low-quality audio waves remains a less indulged topic. In this paper, we propose a novel approach for reconstructing higher frequencies from considerably longer sequences of low-quality MP3 audio waves. Our technique involves inpainting audio spectrograms with residually stacked autoencoder blocks by manipulating individual amplitude and phase values in relation to perceptual differences. Our architecture presents several bottlenecks while preserving the spectral structure of the audio wave via skip-connections. We also compare several task metrics and demonstrate our visual guide to loss selection. Moreover, we show how to leverage differential quantization techniques to reduce the initial model size by more than half while simultaneously reducing inference time, which is crucial in real-world applications.

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