Audio classification is a challenging task that requires categorizing audio data based on its content or characteristics. Existing approaches for audio classification rely either on supervised learning or fine-tuning based on self-supervised learning, both of which require manually labeled data. However, manually labeling audio datasets is a time-consuming and expensive process that limits the dataset's size. Moreover, the diversity of sound categories and class imbalances can further impede classification performance. To overcome these challenges, researchers have proposed various audio data augmentation methods. However, most of these methods focus less on augmentations combination and design and rely solely on waveform-based or spectrogram-based approaches. This paper presents an Automated Audio Augmentation (AAA) method for audio classification, which generates learnable and composable augmentation policies suitable for the audio classification task and can be employed in a plug-and-play manner. This method leverages both waveform-level and spectrogram-level augmentation, and a Bayesian optimization algorithm is proposed to search for composed augmentation policies. To the best of our knowledge, this is the first attempt to propose an automatic data augmentation method for audio classification tasks. Through large-scale empirical studies, we demonstrate that the proposed method outperforms previous competitive methods by a significant margin. We improve the average performance of multiple datasets by 6.421% and by 7.330% on few-shot scenarios, respectively.

This paper presents our solution for the Requests Sub-challenge of the ACM Multimedia 2023 Computational Paralinguistics Challenge. Drawing upon the framework of self-supervised learning, we put forth an automated data augmentation technique for audio classification, accompanied by a multi-channel fusion strategy aimed at enhancing overall performance. Specifically, to tackle the issue of imbalanced classes in complaint classification, we propose an audio data augmentation method that generates appropriate augmentation strategies for the challenge dataset. Furthermore, recognizing the distinctive characteristics of the dual-channel HC-C dataset, we individually evaluate the classification performance of the left channel, right channel, channel difference, and channel sum, subsequently selecting the optimal integration approach. Our approach yields a significant improvement in performance when compared to the competitive baselines, particularly in the context of the complaint task. Moreover, our method demonstrates noteworthy cross-task transferability.

Deep learning (DL) technology is applied to a wide range of intelligent tasks across vision, language, recommendation systems, etc. Large DL models with high sparsity become critical for various intelligent applications and require an energy-efficient hardware accelerator. Sparse-dense matrix multiplication (SpMM) is a key computation kernel widely used in most sparse and large DL workloads. However, traditional computing platforms such as CPU, GPU, and Al chips with regular processing units are limited to support sparsity by their fixed structures. In this work, a specific SpMM accelerator named Sparkle is proposed which achieves high performance and high computational efficiency. A block-wise arrangement approach is proposed in Sparkle to process matrix multiplications. A novel compressed sparsity format, the pointer-bitmap, is designed to simplify the decoding process and improve the efficiency of data loading. Grouped PEs and configurable hierarchical reduction network are deployed to leverage sparsity, further enhancing the utilization of the compute resources. Sparkle is implemented using the Xilinx xqvu11p FPGA. A diverse set of matrices in DL workloads are evaluated and Sparkle achieves 2.1× higher energy efficiency over the NVIDIA TITAN X GPU. Our experiments also show that Sparkle roughly promotes 26% compute efficiency better than state-of-the-art sparse accelerators SIGMA.