Capturing high-frequency data concerning the condition of complex systems, e.g. by acoustic monitoring, has become increasingly prevalent. Such high-frequency signals typically contain time dependencies ranging over different time scales and different types of cyclic behaviors. Processing such signals requires careful feature engineering, particularly the extraction of meaningful time-frequency features. This can be time-consuming and the performance is often dependent on the choice of parameters. To address these limitations, we propose a deep learning framework for learnable wavelet packet transforms, enabling to learn features automatically from data and optimise them with respect to the defined objective function. The learned features can be represented as a spectrogram, containing the important time-frequency information of the dataset. We evaluate the properties and performance of the proposed approach by evaluating its improved spectral leakage and by applying it to an anomaly detection task for acoustic monitoring.
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
Pattern recognition from audio signals is an active research topic encompassing audio tagging, acoustic scene classification, music classification, and other areas. Spectrogram and mel-frequency cepstral coefficients (MFCC) are among the most commonly used features for audio signal analysis and classification. Recently, deep convolutional neural networks (CNN) have been successfully used for audio classification problems using spectrogram-based 2D features. In this paper, we present SpectNet, an integrated front-end layer that extracts spectrogram features within a CNN architecture that can be…
National Chi Nan University（暨南国际大学）；Academia Sinica（中央研究院）
In this paper, we propose a novel speech enhancement (SE) method by exploiting the discrete wavelet transform (DWT). This new method reduces the amount of fast time-varying portion, viz. the DWT-wise detail component, in the spectrogram of speech signals so as to highlight the speech-dominant component and achieves better speech quality. A particularity of this new method is that it is completely unsupervised and requires no prior information about the clean speech and noise in the processed utterance. The presented DWT-based SE method with various scaling factors for the detail part is evalua…
High-Frequency (HF) signals are ubiquitous in the industrial world and are of great use for monitoring of industrial assets. Most deep learning tools are designed for inputs of fixed and/or very limited size and many successful applications of deep learning to the industrial context use as inputs extracted features, which is a manually and often arduously obtained compact representation of the original signal. In this paper, we propose a fully unsupervised deep learning framework that is able to extract a meaningful and sparse representation of raw HF signals. We embed in our architecture impo…
Signal denoising is a key preprocessing step for many applications, as the performance of a learning task is closely related to the quality of the input data. In this paper, we apply a signal processing based deep neural network architecture, a learnable extension of the wavelet packet transform. As main advantages, this model has few parameters, an intuitive initialization and strong learning capabilities. Moreover, we show that it is possible to easily modify the parameters of the model after the training step to tailor to different noise intensities. Two case studies are conducted to compar…
Adaptive ship-radiated noise recognition with learnable fine-grained wavelet transform
Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）；University of Chinese Academy of Sciences（中国科学院大学）
LMD: A Learnable Mask Network to Detect Adversarial Examples for Speaker Verification
School of Marine Science and Technology, Northwestern Polytechnical University（海洋科学与技术学院，西北工业大学）；Research & Development Institute of Northwestern Polytechnical University（西北工业大学研发院）；Department of Electronic Engineering, Tsinghua University（电子工程系，清华大学）；Ocean Institute of Northwestern Polytechnical University（西北工业大学海洋学院）
Detection and Classification of Cetacean Echolocation Clicks using Image-based Object Detection Methods applied to Advanced Wavelet-based Transformations
Center for Safe AGI（安全通用人工智能中心）；Ohla AI Group（Ohla人工智能集团）
Most machine learning models for audio tasks are dealing with a handcrafted feature, the spectrogram. However, it is still unknown whether the spectrogram could be replaced with deep learning based features. In this paper, we answer this question by comparing the different learnable neural networks extracting features with a successful spectrogram model and proposed a General Audio Feature eXtractor (GAFX) based on a dual U-Net (GAFX-U), ResNet (GAFX-R), and Attention (GAFX-A) modules. We design experiments to evaluate this model on the music genre classification task on the GTZAN dataset and …
National Taiwan University（台湾大学）；Academia Sinica（中央研究院）；National Institute of Information and Communications Technology（日本国立信息与通信技术研究所）；National Chi Nan University（暨南国际大学）
For most of the state-of-the-art speech enhancement techniques, a spectrogram is usually preferred than the respective time-domain raw data since it reveals more compact presentation together with conspicuous temporal information over a long time span. However, the short-time Fourier transform (STFT) that creates the spectrogram in general distorts the original signal and thereby limits the capability of the associated speech enhancement techniques. In this study, we propose a novel speech enhancement method that adopts the algorithms of discrete wavelet packet transform (DWPT) and nonnegative…
École de Technologie Supérieure, Université du Québec（魁北克大学高等技术学院）
In this paper we propose a novel environmental sound classification approach incorporating unsupervised feature learning from codebook via spherical $K$-Means++ algorithm and a new architecture for high-level data augmentation. The audio signal is transformed into a 2D representation using a discrete wavelet transform (DWT). The DWT spectrograms are then augmented by a novel architecture for cycle-consistent generative adversarial network. This high-level augmentation bootstraps generated spectrograms in both intra and inter class manners by translating structural features from sample to sampl…
Flatiron Institute（熨斗研究所）；Cornell Lab of Ornithology, Cornell University（康奈尔大学康奈尔鸟类学实验室）；Music and Audio Research Laboratory, New York University（纽约大学音乐与音频研究实验室）；Ecole Normale Supérieure（巴黎高等师范学院）；Collège de France（法兰西公学院）
In time series classification and regression, signals are typically mapped into some intermediate representation used for constructing models. Since the underlying task is often insensitive to time shifts, these representations are required to be time-shift invariant. We introduce the joint time-frequency scattering transform, a time-shift invariant representation which characterizes the multiscale energy distribution of a signal in time and frequency. It is computed through wavelet convolutions and modulus non-linearities and may therefore be implemented as a deep convolutional neural network…
An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies
Queen Mary University of London（伦敦玛丽女王大学）；University of Essex（埃塞克斯大学）；Austrian Institute of Technology（奥地利技术研究所）；Amazon Alexa（亚马逊Alexa）；University of Oxford（牛津大学）
Time-frequency localization of bird calls in dense soundscapes
Acoustic Research Laboratory, National University of Singapore（新加坡国立大学声学研究实验室）；Tropical Marine Science Institute, National University of Singapore（新加坡国立大学热带海洋科学研究所）；School of Marine Science and Technology, Northwestern Polytechnical University（西北工业大学航海学院）
