Filterbank learning for deep neural network based polyphonic sound event detection

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FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, methodology): “In this context, the proposed dataset supports several approaches such as learning sound event representations directly from waveforms [89, 90]; analysis of label noise mitigation methods leveraging the non-exhaustive labeling of the dev set [32, 41, 86];”
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].”
Deep Learning for Audio Signal Processing (2019, methodology): “[18] and [19] use a full-resolution magnitude spectrum, [20]–[23] directly use a raw waveform representation of the audio signals as inputs and learn datadriven filters jointly with the rest of the network for the target tasks.”
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection (2017, unknown): “Non-negative matrix factorization (NMF) based source separation [14] and deep learning based methods (such as feedforward neural networks (FNN) [15], CNN [16] and RNN [11]) have been shown to perform significantly better compared to established methods such as GMM-HMM for polyphonic SED.”