In this paper, we apply convolutional deep belief net- works to audio data and empirically evaluate them on various audio classification tasks In the case of speech
nips AudioConvolutionalDBN
An optimized audio classification and segmentation algorithm is presented in this paper that segments a superimposed audio stream on the basis of its content
The aim of this paper is to present a natural sound detection and classification system In particular, a system that detects and classifies bird and insects sounds
23 jan 2019 · Our aim, in this paper, is to use the deep learning networks for classifying the environmental sounds based on the generated spectrograms of
method to classify audio signal into speech, music and others for the purpose of In this paper, a novel automatic audio classification approach is presented to
pcm audio
In this paper, we explore different approaches in multi-sound classification, and propose a stacked classifier based on the recent advance in deep learning
p haubrick
This paper shows that the non-stationary be- havior lost by MFSC coefficients is captured by a scatter ficient representations for audio classification.
In this paper a high-accuracy algorithm of audio classification is presented. We plan to discriminate different environment sound in a one-to-four-second
In this paper we apply convolutional deep belief net- works to audio data and empirically evaluate them on various audio classification tasks. In the case of
22 juin 2021 In this paper ensembles of ... Keywords: audio classification; data augmentation; ensemble of classifiers; pattern recognition.
This paper shows that the non-stationary be- havior lost by MFSC coefficients is captured by a scatter ficient representations for audio classification.
The paper also provides an evaluation of human accuracy in classifying environmen- tal sounds and compares it to the performance of selected baseline
This paper focuses on audio and speech classification and aims to reduce the need for large amounts of labeled data for the AST by leveraging self-supervised
In this paper we present ensembles of classifiers for automated animal audio classification exploiting different data augmentation techniques for training
classification and extended to use bird audio detection [26] and music emotion recognition [27]. In this paper we introduce CRNNs for environmental.
MULTISCALE SCATTERING FOR AUDIO CLASSIFICATION