Polyphonic sound event detection using multi label deep neural networks

By Emre Çakir, Toni Heittola, Heikki Huttunen, Tuomas Virtanen. 2015

298 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Signal Processing

Citations in the corpus

FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, background): “This initial trend was followed by the rapid adoption of deep learning approaches using fully connected neural networks [17], convolutional neural networks (CNN) [18], recurrent neural networks (RNN) [19], or combinations thereof [20].”
PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition (2019, methodology): “CNN-based systems have achieved state-of-the-art performance in several DCASE challenge tasks including acoustic scene classification [2] and sound event detection [4].”
Filterbank learning for deep neural network based polyphonic sound event detection (2016, methodology): “Some of the conventional acoustic features can be listed as spectral energy, zero-crossing rate [5], (log) mel band energy [6] and melfrequency cepstral coefficients (MFCC) [7].”
SoundNet: Learning Sound Representations from Unlabeled Video (2016, background): “Even though there are unsupervised [20] and supervised [27, 23, 6, 12] deep learning methods applied to sound classification, the models are limited by the amount of available labeled natural sound data.”
Audio-Visual Event Localization in Unconstrained Videos (2018, unknown): “Approaches based on Hidden Markov Models (HMM), Gaussian Mixture Models (GMM), feed-forward Deep Neural Networks (DNN), and Bidirectional Long Short-Term Memory (BLSTM) [15] are developed in [16,17,18,19].”
CNN-Based Learnable Gammatone Filterbank and Equal-Loudness Normalization for Environmental Sound Classification (2020, methodology): “Various algorithms based on signal processing and machine learning have been applied to the problem, including matrix factorization [5]–[7], dictionary learning [8], [9], wavelet ﬁl-terbanks [9], [10], and deep neural networks [11].”
Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks (2018, background): “Some of the classifiers include Gaussian mixture model (GMM) - hidden Markov model (HMM) [27], fully connected (FC) neural networks [28], recurrent neural networks (RNN) [29]–[32], and convolutional neural networks (CNN) [33], [34].”
Recurrent neural networks for polyphonic sound event detection in real life recordings (2016, methodology): “In Table 1 we compare the average scores over all contexts for the FNN in [12] to our BLSTM and LSTM networks trained on the same data, and BLSTM network trained with the augmented data.”
Audio-based multimedia event detection using deep recurrent neural networks (2016, methodology): “In [11], a deep neural network was used to distinguish between four semantic units; a larger repository of 61 semantic units were dealt with in [12] and [13].”
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection (2017, unknown): “The main results with the best performing (based on the validation data) CRNN, CNN, RNN, and the GMM and FNN baselines are reported in Table II.”
Adaptive Pooling Operators for Weakly Labeled Sound Event Detection (2018, background): “The most recent research on SED is dominated by deep (fully connected) neural networks [27], convolutional networks [4], [28], [29], recurrent networks [30], [31], or convolutional-recurrent networks [32], [33].”
Metrics for Polyphonic Sound Event Detection (2016, methodology): “Often used in sound event detection are representations of the signal spectrum, such as mel-frequency cepstral coefficients [25,26], mel energies [17,27], or simply the amplitude or power spectrum [18,28].”
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification (2016, background): “factorization [4]–[6], dictionary learning [7], [8], wavelet filterbanks [8], [9] and most recently deep neural networks [10], [11].”