The paper presents an audio-based context recognition system using ensemble classifiers
with wavelet feature extraction. The device-wise classification accuracy can be achieved …

In this article, we adapted five recent SSL methods to the task of audio classification. The first
two methods, namely Deep Co-Training (DCT) and Mean Teacher (MT), involve two …

In this paper, we present a gated convolutional neural network and a temporal attention-
based localization method for audio classification, which won the 1st place in the large-scale …

In this paper, a random sub-space discriminant classifier for classifying acoustic devices that
combines the features obtained from Mel-frequency cepstral coefficients (MFCCs), and …

In recent years, the use of Deep Learning techniques in audio signal processing has led the
scientific community to develop machine learning strategies that allow to build efficient …

In the context of deep learning, the availability of large amounts of training data can play a
critical role in a model's performance. Recently, several models for audio classification have …

Everything we know is based on our brain's ability to process sensory data. Hearing is a
crucial sense for our ability to learn. Sound is essential for a wide range of activities such as …

Automatic audio event recognition (AER) plays a major role in designing and building
intelligent location and context-aware applications including audio surveillance, audio …

In this work, we propose a multi-head relevance weighting framework to learn audio
representations from raw waveforms. The audio waveform, split into windows of short …

The current methodology in tackling Acoustic Scene Classification (ASC) task can be
described in two steps, preprocessing of the audio waveform into log-mel spectrogram and …