Acoustic scene classification (ASC) is a problem related to the field of machine listening whose objective is to classify/tag an audio clip in a predefined label describing a scene location (e. g. park, airport, etc.). Many state-of-the-art solutions to ASC incorporate data augmentation techniques and model ensembles. However, considerable improvements can also be achieved only by modifying the architecture of convolutional neural networks (CNNs). In this work we propose two novel squeeze-excitation blocks to improve the accuracy of a CNN-based ASC framework based on residual learning. The main idea of squeeze-excitation blocks is to learn spatial and channel-wise feature maps independently instead of jointly as standard CNNs do. This is usually achieved by combining some global grouping operators, linear operators and a final calibration between the input of the block and its learned relationships. The behavior of the block that implements such operators and, therefore, the entire neural network, can be modified depending on the input to the block, the established residual configurations and the selected non-linear activations. The analysis has been carried out using the TAU Urban Acoustic Scenes 2019 dataset presented in the 2019 edition of the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge. All configurations discussed in this document exceed the performance of the baseline proposed by the DCASE organization by 13% percentage points. In turn, the novel configurations proposed in this paper outperform the residual configurations proposed in previous works.

Residual learning is known for being a learning framework that facilitates the training of very deep neural networks. Residual blocks or units are made up of a set of stacked layers, where the inputs are added back to their outputs with the aim of creating identity mappings. In practice, such identity mappings are accomplished by means of the so-called skip or shortcut connections. However, multiple implementation alternatives arise with respect to where such skip connections are applied within the set of stacked layers making up a residual block. While residual networks for image classification using convolutional neural networks (CNNs) have been widely discussed in the literature, their adoption for 1D end-to-end architectures is still scarce in the audio domain. Thus, the suitability of different residual block designs for raw audio classification is partly unknown. The purpose of this paper is to compare, analyze and discuss the performance of several residual block implementations, the most commonly used in image classification problems, within a state-of-the-art CNN-based architecture for end-to-end audio classification using raw audio waveforms. Deep and careful statistical analyses over six different residual block alternatives are conducted, considering two well-known datasets and common input normalization choices. The results show that, while some significant differences in performance are observed among architectures using different residual block designs, the selection of the most suitable residual block can be highly dependent on the input data.

Open-set recognition (OSR) is a challenging machine learning problem that appears when classifiers are faced with test instances from classes not seen during training. It can be summarized as the problem of correctly identifying instances from a known class (seen during training) while rejecting any unknown or unwanted samples (those belonging to unseen classes). Another problem arising in practical scenarios is few-shot learning (FSL), which appears when there is no availability of a large number of positive samples for training a recognition system. Taking these two limitations into account, a new dataset for OSR and FSL for audio data was recently released to promote research on solutions aimed at addressing both limitations. This paper proposes an audio OSR/FSL system divided into three steps: a high-level audio representation, feature embedding using two different autoencoder architectures and a multi-layer perceptron (MLP) trained on latent space representations to detect known classes and reject unwanted ones. An extensive set of experiments is carried out considering multiple combinations of openness factors (OSR condition) and number of shots (FSL condition), showing the validity of the proposed approach and confirming superior performance with respect to a baseline system based on transfer learning.

No abstract

No abstract