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.