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End-to-end environmental sound classification using a 1D convolutional neural network
In this paper, we present an end-to-end approach for environmental sound classification
based on a 1D Convolution Neural Network (CNN) that learns a representation directly from …
based on a 1D Convolution Neural Network (CNN) that learns a representation directly from …
Deep convolutional neural networks and data augmentation for environmental sound classification
The ability of deep convolutional neural networks (CNNs) to learn discriminative spectro-
temporal patterns makes them well suited to environmental sound classification. However …
temporal patterns makes them well suited to environmental sound classification. However …
Adaptive pooling operators for weakly labeled sound event detection
Sound event detection (SED) methods are tasked with labeling segments of audio
recordings by the presence of active sound sources. SED is typically posed as a supervised …
recordings by the presence of active sound sources. SED is typically posed as a supervised …
What's all the fuss about free universal sound separation data?
We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for
experiments in separating mixtures of an unknown number of sounds from an open domain …
experiments in separating mixtures of an unknown number of sounds from an open domain …
Bird detection in audio: a survey and a challenge
Many biological monitoring projects rely on acoustic detection of birds. Despite increasingly
large datasets, this detection is often manual or semi-automatic, requiring manual …
large datasets, this detection is often manual or semi-automatic, requiring manual …
Improving sound event detection in domestic environments using sound separation
N Turpault, S Wisdom, H Erdogan, J Hershey… - ar** target sound events and non-target sounds, also referred to as interference or …
Convolutional recurrent neural networks for urban sound classification using raw waveforms
J Sang, S Park, J Lee - 2018 26th European Signal Processing …, 2018 - ieeexplore.ieee.org
Recent studies have demonstrated deep learning approaches directly from raw data have
been successfully used in image and text. This approach has been applied to audio signals …
been successfully used in image and text. This approach has been applied to audio signals …
CNN-based learnable gammatone filterbank and equal-loudness normalization for environmental sound classification
For environmental sound classification (ESC), this letter presents a learnable auditory
filterbank based on a one-dimensional (1D) convolutional neural network with strong …
filterbank based on a one-dimensional (1D) convolutional neural network with strong …
Acoustic features for environmental sound analysis
Most of the time it is nearly impossible to differentiate between particular type of sound
events from a waveform only. Therefore, frequency-domain and time-frequency domain …
events from a waveform only. Therefore, frequency-domain and time-frequency domain …
Novel TEO-based Gammatone features for environmental sound classification
In this paper, we propose to use modified Gammatone filterbank with Teager Energy
Operator (TEO) for environmental sound classification (ESC) task. TEO can track energy as …
Operator (TEO) for environmental sound classification (ESC) task. TEO can track energy as …