Sound-event classification often utilizes time-frequency analysis, which produces an image-
like spectrogram. Recent approaches such as spectrogram image features and subband …

The ability to automatically recognize a wide range of sound events in real-world conditions
is an important part of applications such as acoustic surveillance and machine hearing. Our …

Classification of complex acoustic scenes under real time scenarios is an active domain
which has engaged several researchers lately form the machine learning community. A …

Urban sound event detection can automatically preload relevant information for a robot to
ensure that it can be applied to various scene-activity tasks. To address the limitations of …

The popular frequency-domain features Mel-frequency cepstral coefficients (MFCCs) have
been widely used for the task of acoustic scene classification (ASC). The MFCC feature …

In this letter, we present a novel feature extraction method for sound event classification,
based on the visual signature extracted from the sound's time-frequency representation. The …

Sound-event classification has emerged as an important field of research in recent years. In
particular, investigations using sound data are being conducted in various industrial fields …

This paper addresses the problem of sound event classification, focusing on feature
extraction methods which are robust in noisy environments. In real world, sound events can …

This work proposes the use of pseudo-color cochleagram image of sound signals for feature
extraction for robust acoustic event recognition. A cochleagram is a variation of the …

This paper proposes an environmental sound segmentation method using Mask U-Net. In
recent years, human–robot interactions, especially speech dialogue, have been assessed …