Deep neural networks are widely used for voiceprint recognition, whilst voiceprint recognition models are vulnerable to attacks. Existing protection schemes for voiceprint recognition models are insufficient to withstand various robustness attacks an...
The detection and classification of engine-based moving objects in restricted scenes from acoustic signals allow better Unmanned Aerial System (UAS)-specific intelligent systems and audio-based surveillance systems. Recurrent Neural Networks (RNNs) p...
We present a deep learning model for the recognition of speech commands in the English language. The dataset is based on the Google Speech Commands Dataset by Warden P., version 0.01, and it consists of ten distinct commands (“left”, &ldq...
Emotion recognition by social robots is a serious challenge because sometimes people also do not cope with it. It is important to use information about emotions from all possible sources: facial expression, speech, or reactions occurring in the body....
Hit Song Science aims to forecast a song’s success before release and benefits from integrating signals beyond audio content alone. We present DeepHits, an end-to-end multimodal network that combines (i) log-Mel spectrogram embeddings from a co...
Denoising diffusion probabilistic models (DDPMs) have proven to be useful in text-to-speech (TTS) tasks; however, it has been a challenge for traditional diffusion models to carry out real-time processing because of the need for hundreds of sampling...
Accurate estimation of aircraft operations, such as takeoffs and landings, is critical for airport planning and resource allocation, yet it remains particularly challenging at non-towered airports, where no dedicated surveillance infrastructure exist...
Speech emotion recognition (SER) is an emerging technology that utilizes speech sounds to identify a speaker’s emotional state. Computational intelligence is receiving increasing attention from academics, health, and social media applications....
Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are esse...
Acoustic side-channel attacks (ASCAs) exploit unintended sound emitted by keyboards to infer typed input, but existing methods generally assume manually labelled training data and controlled environments, limiting their applicability to realistic sce...
Sleep disorders are steadily increasing in the population and can significantly affect daily life. Low-cost and noninvasive systems that can assist the diagnostic process will become increasingly widespread in the coming years. This work aims to inve...
Due to increased air traffic flow, air traffic controllers (ATCs) operate in a state of high load or even overload for long periods of time, which can seriously affect the reliability and efficiency of controllers’ commands. Thus, the early ide...
Speech lie detection is a technique that analyzes speech signals in detail to determine whether a speaker is lying. It has significant application value and has attracted attention from various fields. However, existing speech lie detection algorithm...
Early diagnosis is essential for the effective management of cardiovascular diseases (CVDs). Although conventional auscultation is the primary screening method, its reliance on subjective interpretation and susceptibility to clinical background noise...
Background/Objectives: Suicide remains a major public health concern, with over 700,000 deaths annually worldwide. Current risk assessment is limited by the tendency of at-risk patients to deny suicidal ideation, with denial rates of approximately 50...
With the widespread use of audio editing software and artificial intelligence, it has become very easy to forge audio files. One type of these forgeries is copy-move forgery, which is achieved by copying a segment from an audio file and placing it in...
Understanding the feeding dynamics of aquatic animals is crucial for aquaculture optimization and ecosystem management. This paper proposes a novel framework for analyzing fish feeding behavior based on a fusion of spectrogram-extracted features and...
Alzheimer’s disease (AD) and Parkinson’s disease (PD) are prevalent neurodegenerative disorders among the elderly, leading to cognitive decline and motor impairments. As the population ages, the prevalence of these neurodegenerative disor...
Spectrogram-based representations have grown to dominate the feature space for deep learning audio analysis systems, and are often adopted for speech analysis also. Initially, the primary motivation behind spectrogram-based representations was their...
Internet of things (IoT)-enabled wireless body area network (WBAN) is an emerging technology that combines medical devices, wireless devices, and non-medical devices for healthcare management applications. Speech emotion recognition (SER) is an activ...
Speaker identification is a classification task which aims to identify a subject from a given time-series sequential data. Since the speech signal is a continuous one-dimensional time series, most of the current research methods are based on convolut...
We examine a classification task in which signals of naturally occurring earthquakes are categorized ranging from minor to major, based on their magnitude. Generalized to a single-label classification task, most prior investigations have focused on a...
Power fault monitoring based on acoustic waves has gained a great deal of attention in industry. Existing methods for fault diagnosis typically collect sound signals on site and transmit them to a back-end server for analysis, which may fail to provi...
Voice data contain a wealth of temporal and spectral information and can be a valuable resource for disease classification. However, traditional methods are often not effective in capturing the key features required for the classification of multiple...
Arabic automatic speech recognition (ASR) faces distinct challenges due to its complex morphology, dialectal variations, and the presence of diacritical marks that strongly influence pronunciation and meaning. This study introduces a lightweight appr...
The detection of sensitive content in online videos is a key challenge for ensuring digital safety and effective content moderation. This work proposes the Multimodal Audiovisual Attention (MAV-Att), a multimodal deep learning framework that jointly...
Although recent data-driven Underwater Acoustic Target Recognition (UATR) methods have played a dominant role in marine acoustics, they suffer from complex ocean environments and rather small datasets. To tackle such challenges, researchers have reso...
Recorded audio often contains speech from multiple people in conversation. It is useful to label such signals with speaker turns, noting when each speaker is talking and identifying each speaker. This paper discusses how to process speech signals to...
Pantograph-based electrical current transmission systems are used in electric traction vehicles. The contact surface between the pantograph and the catenary wire experiences mechanical and thermal effects during the train’s movement. Typically,...
Leakage in water supply pipelines remains a significant challenge. It leads to resource and economic waste. Researchers have developed several leak detection methods, including the use of embedded sensors and pressure prediction. The former approach...
This work proposes an approach that uses a feature space by combining the representation obtained in the unsupervised learning process and manually selected features defining the prosody of the utterances. In the experiments, we used two time-frequen...
In recent years, deep learning-based speech synthesis has attracted a lot of attention from the machine learning and speech communities. In this paper, we propose Mixture-TTS, a non-autoregressive speech synthesis model based on mixture alignment mec...
This study compared popular Deep Learning (DL) architectures to classify machining surface roughness using sound and force data. The DL architectures considered in this study include Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), Lon...
We evaluated electrocardiogram (ECG) biometrics using pre-configured models of convolutional neural networks (CNNs) with various time-frequency representations. Biometrics technology records a person’s physical or behavioral characteristics in...
Bird research contributes to understanding species diversity, ecosystem functions, and the maintenance of biodiversity. By analyzing bird images and the audio of birds, we can monitor bird distribution, abundance, and behavior to better understand th...
Musical instrument recognition is a relatively unexplored area of machine learning due to the need to analyze complex spatial–temporal audio features. Traditional methods using individual spectrograms, like STFT, Log-Mel, and MFCC, often miss t...
In this paper, feature extraction methods are developed based on the non-negative matrix factorization (NMF) algorithm to be applied in weakly supervised sound event detection. Recently, the development of various features and systems have been attem...
In this publication, we present a deep learning-based method to transform the
Rope jumping, as a fitness exercise recommended by many sports medicine practitioners, can improve cardiorespiratory capacity and physical coordination. Existing rope jump monitoring systems have limitations in terms of convenience, comfort, and exer...
Text-to-speech synthesis is a computational technique for producing synthetic, human-like speech by a computer. In recent years, speech synthesis techniques have developed, and have been employed in many applications, such as automatic translation ap...
Artificial Neural Network (ANN) models, specifically Convolutional Neural Networks (CNN), were applied to extract emotions based on spectrograms and mel-spectrograms. This study uses spectrograms and mel-spectrograms to investigate which feature extr...
A speech signal can provide various information about a speaker, such as their gender, age, accent, and emotional state. The gender of the speaker is the most salient piece of information contained in the speech signal and is directly or indirectly u...
Honeybee population decline poses a serious threat to global biodiversity and agricultural productivity, underscoring the need for continuous and non-invasive hive monitoring solutions. In particular, early detection of queen absence is critical for...
In environment sound classification, log Mel band energies (MBEs) are considered as the most successful and commonly used features for classification. The underlying algorithm, fast Fourier transform (FFT), is valid under certain restrictions. In thi...
The classification of high-frequency marine mammal vocalizations often faces challenges due to the limitations of acoustic features, which are sensitive to mid-to-low frequencies but offer low resolution in high-frequency ranges. Additionally, single...
Environmental sound classification (ESC) tasks are attracting more and more attention. Due to the complexity of the scene and personnel mobility, there are some difficulties in understanding and generating environmental sound models for ESC tasks. To...
Gunshot sound classification plays a crucial role in public safety, forensic investigations, and intelligent surveillance systems. This study evaluates the performance of deep learning models in classifying firearm sounds by analyzing twelve time&nda...
A person’s voice serves as an indicator of age, as it changes with anatomical and physiological influences throughout their life. Although age prediction is a subject of interest across various disciplines, age-prediction studies using Korean v...
Over the past few years, the study of environmental sound classification (ESC) has become very popular due to the intricate nature of environmental sounds. This paper reports our study on employing various acoustic features aggregation and data enhan...
In the current field of air traffic control speech, there is a lack of effective objective speech quality evaluation methods. This paper proposes a new network framework based on ResNet–BiLSTM to address this issue. Firstly, the mel-spectrogram...
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