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 …

Acoustic scene classification has gained great interests in recent years due to its diverse
applications. Various acoustic and visual features have been proposed and evaluated …

Acoustic scene recognition (ASR), recognizing acoustic environments given an audio
recording of the scene, has a wide range of applications, eg robotic navigation and audio …

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 …

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

This paper introduces the use of local binary patterns (LBP) extracted from a time-frequency
representation (TFR) for acoustic scene classification. As LBP provides a description of the …

ABSTRACT Acoustic Scene Classification (ASC) is receiving wide spread attention due to its
wide variety of applications in smart wearable devices, surveillance, life log diarization etc …

Convolutional Neural Networks (CNNs) have been widely applied to audio classification
recently where promising results have been obtained. Previous CNN-based systems mostly …

Growing demands from applications like surveillance, archiving, and context-aware devices
have fuelled research towards efficient extraction of useful information from environmental …

Classification of environmental sounds plays a key role in security, investigation, robotics
since the study of the sounds present in a specific environment can allow to get significant …