22 research outputs found
A Compact and Discriminative Feature Based on Auditory Summary Statistics for Acoustic Scene Classification
One of the biggest challenges of acoustic scene classification (ASC) is to
find proper features to better represent and characterize environmental sounds.
Environmental sounds generally involve more sound sources while exhibiting less
structure in temporal spectral representations. However, the background of an
acoustic scene exhibits temporal homogeneity in acoustic properties, suggesting
it could be characterized by distribution statistics rather than temporal
details. In this work, we investigated using auditory summary statistics as the
feature for ASC tasks. The inspiration comes from a recent neuroscience study,
which shows the human auditory system tends to perceive sound textures through
time-averaged statistics. Based on these statistics, we further proposed to use
linear discriminant analysis to eliminate redundancies among these statistics
while keeping the discriminative information, providing an extreme com-pact
representation for acoustic scenes. Experimental results show the outstanding
performance of the proposed feature over the conventional handcrafted features.Comment: Accepted as a conference paper of Interspeech 201
Reducing Model Complexity for DNN Based Large-Scale Audio Classification
Audio classification is the task of identifying the sound categories that are
associated with a given audio signal. This paper presents an investigation on
large-scale audio classification based on the recently released AudioSet
database. AudioSet comprises 2 millions of audio samples from YouTube, which
are human-annotated with 527 sound category labels. Audio classification
experiments with the balanced training set and the evaluation set of AudioSet
are carried out by applying different types of neural network models. The
classification performance and the model complexity of these models are
compared and analyzed. While the CNN models show better performance than MLP
and RNN, its model complexity is relatively high and undesirable for practical
use. We propose two different strategies that aim at constructing
low-dimensional embedding feature extractors and hence reducing the number of
model parameters. It is shown that the simplified CNN model has only 1/22 model
parameters of the original model, with only a slight degradation of
performance.Comment: Accepted by ICASSP 201
A survey on artificial intelligence-based acoustic source identification
The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions
Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework
This article proposes an encoder-decoder network model for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. We make use of multiple low-level spectrogram features at the front-end, transformed into higher level features through a well-trained CNN-DNN front-end encoder. The high-level features and their combination (via a trained feature combiner) are then fed into different decoder models comprising random forest regression, DNNs and a mixture of experts, for back-end classification. We conduct extensive experiments to evaluate the performance of this framework on various ASC datasets, including LITIS Rouen and IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 Task 1, 2017 Task 1, 2018 Tasks 1A & 1B and 2019 Tasks 1A & 1B. The experimental results highlight two main contributions; the first is an effective method for high-level feature extraction from multi-spectrogram input via the novel CNN-DNN architecture encoder network, and the second is the proposed decoder which enables the framework to achieve competitive results on various datasets. The fact that a single framework is highly competitive for several different challenges is an indicator of its robustness for performing general ASC tasks
Robust Deep Learning Frameworks for Acoustic Scene and Respiratory Sound Classification
Although research on Acoustic Scene Classification (ASC) is very close to, or even overshadowed by different popular research areas known as Automatic Speech Recognition (ASR), Speaker Recognition (SR) or Image Processing (IP), this field potentially opens up several distinct and meaningful application areas based on environment context detection. The challenges of ASC mainly come from different noise resources, various sounds in real-world environments, occurring as single sounds, continuous sounds or overlapping sounds. In comparison to speech, sound scenes are more challenging mainly due to their being unstructured in form and closely similar to noise in certain contexts. Although a wide range of publications have focused on ASC recently, they show task-specific ways that either explore certain aspects of an ASC system or are evaluated on limited acoustic scene datasets. Therefore, the aim of this thesis is to contribute to the development of a robust framework to be applied for ASC, evaluated on various recently published datasets, and to achieve competitive performance compared to the state-of-the-art systems. To do this, a baseline model is firstly introduced. Next, extensive experiments on the baseline are conducted to identify key factors affecting final classification accuracy. From the comprehensive analysis, a robust deep learning framework, namely the Encoder-Decoder structure, is proposed to address three main factors that directly affect an ASC system. These factors comprise low-level input features, high-level feature extraction methodologies, and architectures for final classification. Within the proposed framework, three spectrogram transformations, namely Constant Q Transform (CQT), gammatone filter (Gamma), and log-mel, are used to convert recorded audio signals into spectrogram representations that resemble two-dimensional images. These three spectrograms used are referred to as low-level input features. To extract high-level features from spectrograms, a novel Encoder architecture, based on Convolutional Neural Networks, is proposed. In terms of the Decoder, also referred as to the final classifier, various models such as Random Forest Classifier, Deep Neural Network and Mixture of Experts, are evaluated and structured to obtain the best performance. To further improve an ASC system's performance, a scheme of two-level hierarchical classification, replacing the role of Decoder classification recently mentioned, is proposed. This scheme is useful to transform an ASC task over all categories into multiple ASC sub-tasks, each spanning fewer categories, in a divide-and- conquer strategy. At the highest level of the proposed scheme, meta-categories of acoustic scene sounds showing similar characteristics are classified. Next, categories within each meta-category are classified at the second level. Furthermore, an analysis of loss functions applied to different classifiers is conducted. This analysis indicates that a combination of entropy loss and triplet loss is useful to enhance performance, especially with tasks that comprise fewer categories. Further exploring ASC in terms of potential application to the health services, this thesis also explores the 2017 Internal Conference on Biomedical Health Informatics (ICBHI) benchmark dataset of lung sounds. A deep-learning frame- work, based on our novel ASC approaches, is proposed to classify anomaly cycles and predict respiratory diseases. The results obtained from these experiments show exceptional performance. This highlights the potential applications of using advanced ASC frameworks for early detection of auditory signals. In this case, signs of respiratory diseases, which could potentially be highly useful in future in directing treatment and preventing their spread
Audio Deepfake Detection: A Survey
Audio deepfake detection is an emerging active topic. A growing number of
literatures have aimed to study deepfake detection algorithms and achieved
effective performance, the problem of which is far from being solved. Although
there are some review literatures, there has been no comprehensive survey that
provides researchers with a systematic overview of these developments with a
unified evaluation. Accordingly, in this survey paper, we first highlight the
key differences across various types of deepfake audio, then outline and
analyse competitions, datasets, features, classifications, and evaluation of
state-of-the-art approaches. For each aspect, the basic techniques, advanced
developments and major challenges are discussed. In addition, we perform a
unified comparison of representative features and classifiers on ASVspoof 2021,
ADD 2023 and In-the-Wild datasets for audio deepfake detection, respectively.
The survey shows that future research should address the lack of large scale
datasets in the wild, poor generalization of existing detection methods to
unknown fake attacks, as well as interpretability of detection results
Sound Processing for Autonomous Driving
Nowadays, a variety of intelligent systems for autonomous driving have been developed, which have already shown a very high level of capability. One of the prerequisites for autonomous driving is an accurate and reliable representation of the environment around the vehicle. Current systems rely on cameras, RADAR, and LiDAR to capture the visual environment and to locate and track other traffic participants. Human drivers, in addition to vision, have hearing and use a lot of auditory information to understand the environment in addition to visual cues. In this thesis, we present the sound signal processing system for auditory based environment representation.
Sound propagation is less dependent on occlusion than all other types of sensors and in some situations is less sensitive to different types of weather conditions such as snow, ice, fog or rain. Various audio processing algorithms provide the detection and classification of different audio signals specific to certain types of vehicles, as well as localization.
First, the ambient sound is classified into fourteen major categories consisting of traffic objects and actions performed. Additionally, the classification of three specific types of emergency vehicles sirens is provided. Secondly, each object is localized using a combined localization algorithm based on time difference of arrival and amplitude. The system is evaluated on real data with a focus on reliable detection and accurate localization of emergency vehicles. On the third stage the possibility of visualizing the sound source on the image from the autonomous vehicle camera system is provided. For this purpose, a method for camera to microphones calibration has been developed.
The presented approaches and methods have great potential to increase the accuracy of environment perception and, consequently, to improve the reliability and safety of autonomous driving systems in general