Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms

Recent work has shown that the end-to-end approach using convolutional neural network (CNN) is effective in various types of machine learning tasks. For audio signals, the approach takes raw waveforms as input using an 1-D convolution layer. In this paper, we improve the 1-D CNN architecture for music auto-tagging by adopting building blocks from state-of-the-art image classification models, ResNets and SENets, and adding multi-level feature aggregation to it. We compare different combinations of the modules in building CNN architectures. The results show that they achieve significant improvements over previous state-of-the-art models on the MagnaTagATune dataset and comparable results on Million Song Dataset. Furthermore, we analyze and visualize our model to show how the 1-D CNN operates.Comment: Accepted for publication at ICASSP 201

Synthia's Melody: A Benchmark Framework for Unsupervised Domain Adaptation in Audio

Despite significant advancements in deep learning for vision and natural language, unsupervised domain adaptation in audio remains relatively unexplored. We, in part, attribute this to the lack of an appropriate benchmark dataset. To address this gap, we present Synthia's melody, a novel audio data generation framework capable of simulating an infinite variety of 4-second melodies with user-specified confounding structures characterised by musical keys, timbre, and loudness. Unlike existing datasets collected under observational settings, Synthia's melody is free of unobserved biases, ensuring the reproducibility and comparability of experiments. To showcase its utility, we generate two types of distribution shifts-domain shift and sample selection bias-and evaluate the performance of acoustic deep learning models under these shifts. Our evaluations reveal that Synthia's melody provides a robust testbed for examining the susceptibility of these models to varying levels of distribution shift

Low-Resource Music Genre Classification with Advanced Neural Model Reprogramming

Transfer learning (TL) approaches have shown promising results when handling tasks with limited training data. However, considerable memory and computational resources are often required for fine-tuning pre-trained neural networks with target domain data. In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR). NMR aims at re-purposing a pre-trained model from a source domain to a target domain by modifying the input of a frozen pre-trained model. In addition to the known, input-independent, reprogramming method, we propose an advanced reprogramming paradigm: Input-dependent NMR, to increase adaptability to complex input data such as musical audio. Experimental results suggest that a neural model pre-trained on large-scale datasets can successfully perform music genre classification by using this reprogramming method. The two proposed Input-dependent NMR TL methods outperform fine-tuning-based TL methods on a small genre classification dataset.Comment: Submitted to ICASSP 2023. Some experimental results were reduced due to the space limit. The implementation will be available at https://github.com/biboamy/music-repr

Automated Detection of COVID-19 Cough Sound using Mel-Spectrogram Images and Convolutional Neural Network

COVID-19 is a new disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant. The initial symptoms of the disease commonly include fever (83-98%), fatigue or myalgia, dry cough (76-82%), and shortness of breath (31-55%). Given the prevalence of coughing as a symptom, artificial intelligence has been employed to detect COVID-19 based on cough sounds. This study aims to compare the performance of six different Convolutional Neural Network (CNN) models (VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152) in detecting COVID-19 using mel-spectrogram images derived from cough sounds. The training and validation of these CNN models were conducted using the Virufy dataset, consisting of 121 cough audio recordings with a sample rate of 48,000 and a duration of 1 second for all audio data. Audio data was processed to generate mel-spectrogram images, which were subsequently employed as inputs for the CNN models. This study used accuracy, area under curve (AUC), precision, recall, and F1 score as evaluation metrics. The AlexNet model, utilizing an input size of 227×227, exhibited the best performance with the highest Area Under the Curve (AUC) value of 0.930. This study provides compelling evidence of the efficacy of CNN models in detecting COVID-19 based on cough sounds through mel-spectrogram images. Furthermore, the study underscores the impact of input size on model performance. This research contributes to identifying the CNN model that demonstrates the best performance in COVID-19 detection based on cough sounds. By exploring the effectiveness of CNN models with different mel-spectrogram image sizes, this study offers novel insights into the optimal and fast audio-based method for early detection of COVID-19. Additionally, this study establishes the fundamental groundwork for selecting an appropriate CNN methodology for early detection of COVID-19

Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learning

The current learning process of deep learning, regardless of any deep neural network (DNN) architecture and/or learning algorithm used, is essentially a single resolution training. We explore multiresolution learning and show that multiresolution learning can significantly improve robustness of DNN models for both 1D signal and 2D signal (image) prediction problems. We demonstrate this improvement in terms of both noise and adversarial robustness as well as with small training dataset size. Our results also suggest that it may not be necessary to trade standard accuracy for robustness with multiresolution learning, which is, interestingly, contrary to the observation obtained from the traditional single resolution learning setting