Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection

Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks (CNN) are able to extract higher level features that are invariant to local spectral and temporal variations. Recurrent neural networks (RNNs) are powerful in learning the longer term temporal context in the audio signals. CNNs and RNNs as classifiers have recently shown improved performances over established methods in various sound recognition tasks. We combine these two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it on a polyphonic sound event detection task. We compare the performance of the proposed CRNN method with CNN, RNN, and other established methods, and observe a considerable improvement for four different datasets consisting of everyday sound events.Comment: Accepted for IEEE Transactions on Audio, Speech and Language Processing, Special Issue on Sound Scene and Event Analysi

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Polyphonic Sound Event Detection by using Capsule Neural Networks

Artificial sound event detection (SED) has the aim to mimic the human ability to perceive and understand what is happening in the surroundings. Nowadays, Deep Learning offers valuable techniques for this goal such as Convolutional Neural Networks (CNNs). The Capsule Neural Network (CapsNet) architecture has been recently introduced in the image processing field with the intent to overcome some of the known limitations of CNNs, specifically regarding the scarce robustness to affine transformations (i.e., perspective, size, orientation) and the detection of overlapped images. This motivated the authors to employ CapsNets to deal with the polyphonic-SED task, in which multiple sound events occur simultaneously. Specifically, we propose to exploit the capsule units to represent a set of distinctive properties for each individual sound event. Capsule units are connected through a so-called "dynamic routing" that encourages learning part-whole relationships and improves the detection performance in a polyphonic context. This paper reports extensive evaluations carried out on three publicly available datasets, showing how the CapsNet-based algorithm not only outperforms standard CNNs but also allows to achieve the best results with respect to the state of the art algorithms

Capsule Routing for Sound Event Detection

The detection of acoustic scenes is a challenging problem in which environmental sound events must be detected from a given audio signal. This includes classifying the events as well as estimating their onset and offset times. We approach this problem with a neural network architecture that uses the recently-proposed capsule routing mechanism. A capsule is a group of activation units representing a set of properties for an entity of interest, and the purpose of routing is to identify part-whole relationships between capsules. That is, a capsule in one layer is assumed to belong to a capsule in the layer above in terms of the entity being represented. Using capsule routing, we wish to train a network that can learn global coherence implicitly, thereby improving generalization performance. Our proposed method is evaluated on Task 4 of the DCASE 2017 challenge. Results show that classification performance is state-of-the-art, achieving an F-score of 58.6%. In addition, overfitting is reduced considerably compared to other architectures.Comment: Paper accepted for 26th European Signal Processing Conference (EUSIPCO 2018

SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks

The understanding of the surrounding environment plays a critical role in autonomous robotic systems, such as self-driving cars. Extensive research has been carried out concerning visual perception. Yet, to obtain a more complete perception of the environment, autonomous systems of the future should also take acoustic information into account. Recent sound event localization and detection (SELD) frameworks utilize convolutional recurrent neural networks (CRNNs). However, considering the recurrent nature of CRNNs, it becomes challenging to implement them efficiently on embedded hardware. Not only are their computations strenuous to parallelize, but they also require high memory bandwidth and large memory buffers. In this work, we develop a more robust and hardware-friendly novel architecture based on a temporal convolutional network(TCN). The proposed framework (SELD-TCN) outperforms the state-of-the-art SELDnet performance on four different datasets. Moreover, SELD-TCN achieves 4x faster training time per epoch and 40x faster inference time on an ordinary graphics processing unit (GPU).Comment: 5 pages, 3 tables, 2 figures. Submitted to EUSIPCO 202

Deep Neural Networks for Sound Event Detection

The objective of this thesis is to develop novel classification and feature learning techniques for the task of sound event detection (SED) in real-world environments. Throughout their lives, humans experience a consistent learning process on how to assign meanings to sounds. Thanks to this, most of the humans can easily recognize the sound of a thunder, dog bark, door bell, bird singing etc. In this work, we aim to develop systems that can automatically detect the sound events commonly present in our daily lives. Such systems can be utilized in e.g. contextaware devices, acoustic surveillance, bio-acoustical and healthcare monitoring, and smart-home cities.In this thesis, we propose to apply the modern machine learning methods called deep learning for SED. The relationship between the commonly used timefrequency representations for SED (such as mel spectrogram and magnitude spectrogram) and the target sound event labels are highly complex. Deep learning methods such as deep neural networks (DNN) utilize a layered structure of units to extract features from the given sound representation input with increased abstraction at each layer. This increases the network’s capacity to efficiently learn the highly complex relationship between the sound representation and the target sound event labels. We found that the proposed DNN approach performs significantly better than the established classifier techniques for SED such as Gaussian mixture models.In a time-frequency representation of an audio recording, a sound event can often be recognized as a distinct pattern that may exhibit shifts in both dimensions. The intra-class variability of the sound events may cause to small shifts in the frequency domain content, and the time domain shift results from the fact that a sound event can occur at any time for a given audio recording. We found that convolutional neural networks (CNN) are useful to learn shift-invariant filters that are essential for robust modeling of sound events. In addition, we show that recurrent neural networks (RNN) are effective in modeling the long-term temporal characteristics of the sound events. Finally, we combine the convolutional and recurrent layers in a single classifier called convolutional recurrent neural networks (CRNN), which emphasizes the benefits of both and provides state-of-the-art results in multiple SED benchmark datasets.Aside from learning the mappings between the time-frequency representations and the sound event labels, we show that deep learning methods can also be utilized to learn a direct mapping between the the target labels and a lower level representation such as the magnitude spectrogram or even the raw audio signals. In this thesis, the feature learning capabilities of the deep learning methods and the empirical knowledge on the human auditory perception are proposed to be integrated through the means of layer weight initialization with filterbank coefficients. This results with an optimal, ad-hoc filterbank that is obtained through gradient based optimization of the original coefficients to improve the SED performance

A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor

In this paper, a siamese DNN model is proposed to learn the characteristics of the audio dynamic range compressor (DRC). This facilitates an intelligent control system that uses audio examples to configure the DRC, a widely used non-linear audio signal conditioning technique in the areas of music production, speech communication and broadcasting. Several alternative siamese DNN architectures are proposed to learn feature embeddings that can characterise subtle effects due to dynamic range compression. These models are compared with each other as well as handcrafted features proposed in previous work. The evaluation of the relations between the hyperparameters of DNN and DRC parameters are also provided. The best model is able to produce a universal feature embedding that is capable of predicting multiple DRC parameters simultaneously, which is a significant improvement from our previous research. The feature embedding shows better performance than handcrafted audio features when predicting DRC parameters for both mono-instrument audio loops and polyphonic music pieces.Comment: 8 pages, accepted in IJCNN 201