An investigation of the reconstruction capacity of stacked convolutional autoencoders for log-mel-spectrograms

TL;DR

This paper explores the use of stacked convolutional autoencoders for compressing and reconstructing monophonic harmonic sounds from log-mel-spectrograms, demonstrating effective unsupervised audio representation learning.

Contribution

It introduces a novel application of convolutional autoencoders for audio compression and proposes an evaluation metric based on frequency accuracy for harmonic sound reconstruction.

Findings

Autoencoders successfully reconstruct harmonic sounds from compressed representations

Hyper-parameter tuning improves reconstruction quality

Frequency accuracy correlates with perceived sound quality

Abstract

In audio processing applications, the generation of expressive sounds based on high-level representations demonstrates a high demand. These representations can be used to manipulate the timbre and influence the synthesis of creative instrumental notes. Modern algorithms, such as neural networks, have inspired the development of expressive synthesizers based on musical instrument timbre compression. Unsupervised deep learning methods can achieve audio compression by training the network to learn a mapping from waveforms or spectrograms to low-dimensional representations. This study investigates the use of stacked convolutional autoencoders for the compression of time-frequency audio representations for a variety of instruments for a single pitch. Further exploration of hyper-parameters and regularization techniques is demonstrated to enhance the performance of the initial design. In an unsupervised manner, the network is able to reconstruct a monophonic and harmonic sound based on latent representations. In addition, we introduce an evaluation metric to measure the similarity between the original and reconstructed samples. Evaluating a deep generative model for the synthesis of sound is a challenging task. Our approach is based on the accuracy of the generated frequencies as it presents a significant metric for the perception of harmonic sounds. This work is expected to accelerate future experiments on audio compression using neural autoencoders.