The performance of speaker recognition systems is very well on the datasets without noise and mismatch. However, the performance gets degraded with the environmental noises, channel variation, physical and behavioral changes in speaker. The types of...
This work propose an audio feature pipeline to support machine learning tasks through the extraction of the Mel Frequency Cepstral Coefficients and Mel-spectrogram which is then used as the input of an Convolutional Neural Network which is trained to...
Predicting the remaining time before the next earthquake based on seismic signals generated in a laboratory setting is a challenging research task that is of significant importance for earthquake hazard assessment. In this study, we employed a mel sp...
An elbow wall thinning diagnosis method by highlighting the stationary characteristics of the operating loop is proposed. The accelerations of curved pipe surfaces were measured in a closed test loop operating at a constant pump rpm, combined with cu...
The existing research on emotion recognition commonly uses mel spectrogram (MelSpec) and Geneva minimalistic acoustic parameter set (GeMAPS) as acoustic parameters to learn the audio features. MelSpec can represent the time-series variations of each...
Environmental noise is inevitable during seismic data acquisition, with major sources including heavy machinery, rivers, wind, and other environmental factors. During field data acquisition, it is important to assess the impact of environmental noise...
This study presents a novel approach to the end-of-line (EoL) quality inspection of brushless DC (BLDC) motors by implementing a deep learning model that combines MEL diagrams, convolutional neural networks (CNNs) and bidirectional gated recurrent un...
In response to the problem of complex and variable loads on wind turbine gearbox bearing in working conditions, as well as the limited amount of sound data making fault identification difficult, this study focuses on sound signals and proposes an int...
Background/Objectives: Automatic pain detection from speech signals holds strong promise for non-invasive and real-time assessment in clinical and caregiving settings, particularly for populations with limited capacity for self-report. Methods: In th...
Audio signals play a crucial role in our perception of our surroundings. People rely on sound to assess motion, distance, direction, and environmental conditions, aiding in danger avoidance and decision making. However, in real-world environments, du...
Automatic piano transcription (APT) is a challenging problem in music information retrieval. In recent years, most APT approaches have been based on neural networks and have demonstrated higher performance. However, most previous works utilize a shor...
This paper presents an automatic system for the classification of musical instruments from audio recordings. The project leverages deep learning (DL) techniques to achieve its objective, exploring three different classification approaches based on di...
Respiratory disease classification using lung sound recordings remains challenging due to signal interference, heterogeneous acquisition conditions, and substantial overlap among clinically related acoustic patterns. This study presents a framework f...
Speech emotion recognition (SER) is not only a ubiquitous aspect of everyday communication, but also a central focus in the field of human–computer interaction. However, SER faces several challenges, including difficulties in detecting subtle e...
In this paper, an intelligent localization framework based on deep learning is proposed to address the limitations of insufficient accuracy and robustness in defect identification and localization during the ultrasonic guided-wave non-destructive tes...
Stuttering, a prevalent neurodevelopmental disorder, profoundly affects fluent speech, causing involuntary interruptions and recurrent sound patterns. This study addresses the critical need for the accurate classification of stuttering types. The res...
In the background of artificial intelligence, the realization of smooth communication between people and machines has become the goal pursued by people. Mel spectrograms is a common method used in speech emotion recognition, focusing on the low-frequ...
Acoustic data inherently contain a variety of information, including indicators of abnormal symptoms. In this study, we propose a method for detecting abnormal symptoms by converting acoustic data into spectrogram representations and applying a deep...
Passive acoustic monitoring (PAM) using hydrophones enables collecting acoustic data to be collected in large and diverse quantities, necessitating the need for a reliable automated classification system. This paper presents a data-processing pipelin...
Text-to-Speech (TTS) methods typically employ a sequential approach with an Acoustic Model (AM) and a vocoder, using a Mel spectrogram as an intermediate representation. However, in home environments, TTS systems often struggle with issues such as in...
An important hurdle in medical diagnostics is the high-quality and interpretable classification of audio signals. In this study, we present an image-based representation of infant crying audio files to predict abnormal infant cries using a vision tra...
The problem of accent recognition has received a lot of attention with the development of Automatic Speech Recognition (ASR) systems. The crux of the problem is that conventional acoustic language models adapted to fit standard language corpora are u...
The CMT welding process has been widely used for aluminum alloy welding. The weld’s penetration state is essential for evaluating the welding quality. Arc sound signals contain a wealth of information related to the penetration state of the wel...
Sound event detection models commonly rely on spectrogram representations of audio signals and recent approaches have adapted image-based object detection architectures to acoustic domains. This paradigm is suitable for respiratory monitoring, where...
Pipeline transportation is the main method for long-distance gas transportation; however, ponding in the pipeline can affect transportation efficiency and even cause corrosion to the pipeline in some cases. A non-destructive method to detect pipeline...
Music genre classification has a significant role in information retrieval for the organization of growing collections of music. It is challenging to classify music with reliable accuracy. Many methods have utilized handcrafted features to identify u...
Speech emotion recognition (SER) technology is significant for human–computer interaction, and this paper studies the features and modeling of SER. Mel-spectrogram is introduced and utilized as the feature of speech, and the theory and extracti...
Speech emotion recognition (SER) plays a vital role in enhancing human–computer interaction (HCI) and can be applied in affective computing, virtual support, and healthcare. This research presents a high-performance SER framework based on a lig...
Speech emotion recognition (SER) classifies emotions using low-level features or a spectrogram of an utterance. When SER methods are trained and tested using different datasets, they have shown performance reduction. Cross-corpus SER research identif...
Refractory materials are basic materials widely used in industrial furnaces and thermal equipment. Their microstructure is similar to that of many heterogeneous high-performance materials used in micro/nanodevices. The presence of damage can reduce t...
This paper presents a sound source distance estimation (SSDE) method using a convolutional recurrent neural network (CRNN). We approach the sound source distance estimation task as an image classification problem, and we aim to classify a given audio...
End-of-Line (EoL) quality inspection of electric motors requires reliable detection of manufacturing faults before products leave the production line. However, conventional supervised deep learning approaches depend on a sufficient number of labeled...
Very-low-frequency (VLF) passive sonar detection is one of the core technologies for maritime surveillance, although its performance is often severely affected by strong impulsive ocean ambient noise interference. This paper, for the first time, prop...
Deep learning models can produce unstable results by introducing imperceptible perturbations that are difficult for humans to recognize. This can have a significant impact on the accuracy and security of deep learning applications due to their poorly...
The sounds of certain industrial products (machines) carry important information about these products. Product classification or malfunction detection can be performed utilizing a product’s sound. In this regard, sound can be used as it is or it can...
Speech Emotion Recognition (SER) plays a vital role in supporting applications such as healthcare, human–computer interaction, and security. However, many existing approaches still face challenges in achieving robust generalization and maintain...
For Thai vowel pronunciation, it is very important to know that when mispronunciation occurs, the meanings of words change completely. Thus, effective and standardized practice is essential to pronouncing words correctly as a native speaker. Since th...
The use of the mel spectrogram as a signal parameterization for voice generation is quite recent and linked to the development of neural vocoders. These are deep neural networks that allow reconstructing high-quality speech from a given mel spectrogr...
Speech deception detection often suffers from overly narrow feature representations and limited robustness under changing recording conditions. To address these limitations, we propose MFF-Net, a multi-feature fusion network with three modules. The f...
This paper presents several machine learning methods and highlights the most effective one for detecting elephant rumbles in infrasonic seismic signals. The design and implementation of electronic circuitry to amplify, filter, and digitize the seismi...
The presented paper is concerned with detection of presentation attacks against unsupervised remote biometric speaker verification, using a well-known challenge–response scheme. We propose a novel approach to convolutional phoneme classifier tr...
Identifying instruments in polyphonic audio is challenging due to overlapping spectra and variations in timbre and playing styles. This task is central to music information retrieval, with applications in transcription, recommendation, and indexing....
Distinguishing between a dangerous audio event like a gun firing and other non-life-threatening events, such as a plastic bag bursting, can mean the difference between life and death and, therefore, the necessary and unnecessary deployment of public...
In this study, we present a pilot investigation using a single Purple Heart plant (Tradescantia pallida) to explore whether bioelectrical signals for dual-purpose classification tasks: environmental state detection and human emotion recognition. Usin...
Music genre classification (MGC) is the basis for the efficient organization, retrieval, and recommendation of music resources, so it has important research value. Convolutional neural networks (CNNs) have been widely used in MGC and achieved excelle...
Acoustic gait recognition is an emerging passive biometric modality that identifies individuals by unique walking sound patterns. This work presents a reproducible benchmarking framework for convolutional neural network (CNN)-based acoustic gait reco...
Recent successes in deep learning have inspired researchers to apply deep neural networks to Acoustic Event Classification (AEC). While deep learning methods can train effective AEC models, they are susceptible to overfitting due to the models’...
Speaker recognition models are typically enrolled using neutral speech, yet real users rarely speak under emotionally neutral conditions. Emotion alters prosody, spectral structure, articulation, and speaking rate, shifting utterances away from the a...
This study rigorously evaluates 13 Convolutional Neural Network (CNN) architectures for Thai dialect recognition. By treating Automatic Speech Recognition (ASR) as a computer vision texture classification task, we processed an extensive 840-h dataset...
Speech impairments often emerge as one of the primary indicators of Parkinson’s disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use o...
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