Audio Signal Processing Research Articles

Application of LSTM Feature Extraction Model in the Construction of Opera Audio Database

Chinese traditional music plays a greater role in ideal society governed by rites and rituals. The musical culture of China is both ancient and modern. Throughout history, musicians have created several musical genres and used countless instruments. In order to appreciate Chinese traditional music, it is helpful to have a basic familiarity with three "keynotes": the classical traditional music of the elites; folk and ethnic music, and the most popular traditional Chinese instruments. In general, an opera audio database consists of audio recordings of opera performances. These recordings may be utilized for a variety of reasons, including research, education, and entertainment. To establish such a database, pertinent features must be extracted from the audio recordings to allow for effective storage, retrieval, and analysis of the data. Improvements in audio signal processing over the last several years have qualified for creating feature extraction methods that improve the quality and accuracy of the retrieved features. We first gather the audio recordings of opera performances using an appropriate recording device to build an opera audio database. Data preprocessing then eliminates noise and other undesired signals from the audio recordings to enhance the data quality. After that, to extract useful features from the audio recordings that may be utilized to define the sound's rates, the Long Short Term Memory (LSTM) technique is used. The components must then be organized and stored in a database for fast retrieval and analysis once extracted. A suitable database management system may be used to build the database. The database should be created to make it simple to search and filter the data using different criteria. The study results stated that building an opera audio database using novel feature extraction approaches is a challenging process that requires an understanding of opera music, database administration, and audio signal processing. The experimental results stated that the proposed model has provided an accuracy of 96%.

NEURAL NETWORKS IN THE TASK OF GENRE CLASSIFICATION OF MUSICAL COMPOSITIONS

This study investigates the application of neural networks in the task of classifying audio signals into ten different genres. The peculiarities of processing audio signals in the digital environment are examined, along with the relationship between Fourier transformation and spectrograms, and the characteristics of audio signals. Neural network training was conducted using the GTZAN dataset, which contains 1000 compositions. Four comparable datasets were formed based on this dataset, and the performance of three neural network architectures - convolutional, recurrent, and multilayer perceptron - was evaluated on each of them. The practical significance of this work lies in the possibility of forming musical recommendations and organizing music. The goal of the study is to develop a classifier that could accurately determine the probability of a composition belonging to one of the ten genres.

An Integrated Thresholding and Morphological Process with Histogram-based Method for Brain Tumor Analysis and MRI Tumor Detection

INTRODUCTION: Over the past several years analysis of image has moved from larger system to pervasive portable devices. For example, in pervasive biomedical systems like PACS-Picture achieving and Communication system, computing is the main element. Image processing application for biomedical diagnosis needs efficient and fast algorithms and architecture for their functionality. Future pervasive systems designed for biomedical application should provide computational efficiency and portability. The discrete wavelet transform (DWT) designed in on-chip been used in several applications like data, audio signal processing and machine learning.
 OBJECTIVES: The conventional convolution based scheme is easy to implement but occupies more memory , power and delay. The conventional lifting based architecture has multiplier blocks which increase the critical delay. Designing the wavelet transform without multiplier is a effective task especially for the 2-D image analysis. Without multiplier Daubechies wavelet implementation in forward and inverse transforms may find efficient. The objective of the work is on obtaining low power and less delay architecture.
 METHODS: The proposed lifting scheme for two dimensional architecture reduces critical path through multiplier less and provides low power, area and high throughput. The proposed multiplier is delay efficient.
 RESULTS: The architecture is Multiplier less in the predict and update stage and the implementation carried out in FPGA by the use of Quartus II 9.1 and it is found that there is reduction in consumption of power at approximately 56%. There is reduction in delay due to multiplier less architecture.
 CONCLUSION: multiplier less architecture provides less delay and low power. The power observed is in milliwatts and suitable for high speed application due to low critical path delay.

Development of non-destructive system for estimating avocado quality parameters

México has established itself as a significant global exporter of avocados, owing to the surging demand for this highly sought-after fruit. In response to market requirements, the assurance of specific quality parameters during the harvesting and international transportation phases has become indispensable. However, the predominant techniques employed for the assessment of these critical quality attributes are often intrusive and labor-intensive, resulting in substantial economic losses. In this research, we propose a novel approach, Smart Platform for Avocado Inspection and Analysis (SPAIA), which leverages intelligent audio analysis to nondestructively estimate key avocado quality parameters. Our methodology comprises a multi-faceted process, encompassing the design and development of a specialized capture device, meticulous data collection, and the formulation of robust audio-based estimation and evaluation models. As a result, our research proposes new aFI, aFIsum indices from Audio Signal Processing (ASP) techniques, and a new spectrogram, a product of image processing techniques and Intelligent Audio Analysis (IAA). Finally, the non-destructive approach yielded remarkable results in the estimation of the avocado quality parameters Seed Weight (SW), Dry Matter Index (DMI), Ripeness State (RS) and Uniform Ripeness (UR). The best regression models achieved a Pearson’s correlation index of 0.988, while the classification models exhibited an accuracy of 0.960. These findings not only demonstrate the viability of SPAIA as an innovative tool but also highlight its potential to revolutionize the avocado industry by offering a more efficient and sustainable means of ensuring fruit quality throughout the entire production and distribution process.

New Advances in Audio Signal Processing

The growth in computing capabilities has significantly transformed the realm of data analysis and processing, most notably through the widespread adoption of artificial intelligence (AI) and deep learning technologies [...]

Realization of adaptive beamformer on open-source hardware

Realization of an efficient system for extracting useful audio signals in the presence of interferences is an important engineering problem in the field of acoustics. This paper proposes a method for extracting sound signals from a specific direction of incidence. A microphone array consisting of eight microphones was used, coupled with space-time signal processing executed in real time on hardware. The microphone signals are filtered by an adaptive beamformer, whose coefficients are optimized in real time on open-source hardware using the Least Mean Square algorithm. This paper describes the characteristics and limitations of the used microphone array. Testing the system through simulation indicates that extracting narrowband and wideband signals using the presented adaptive beamforming method is theoretically possible. This paper aims to experimentally evaluate the algorithm under real-world conditions, as well as identify the limitations of extracting wideband signals. Adaptation of the filter coefficients and filtering the signals from the microphones was implemented on Bela hardware, specialized for processing audio signals. This experiment highlights the limitations that arise from extracting a wideband signal using this type of hardware in real conditions. By comparing the useful and filtered signals in both time and frequency domains, the quality of the filtering was analyzed. Our experiments suggest that the current hardware specifications are a limiting factor for successful wideband signal filtering in real conditions, as they limit the maximum possible order of the adaptive filter, which proved to be insufficient.

Efficient FPGA implementation for sound source separation using direction-informed multichannel non-negative matrix factorization

Sound source separation (SSS) is a fundamental problem in audio signal processing, aiming to recover individual audio sources from a given mixture. A promising approach is multichannel non-negative matrix factorization (MNMF), which employs a Gaussian probabilistic model encoding both magnitude correlations and phase differences between channels through spatial covariance matrices (SCM). In this work, we present a dedicated hardware architecture implemented on field programmable gate arrays (FPGAs) for efficient SSS using MNMF-based techniques. A novel decorrelation constraint is presented to facilitate the factorization of the SCM signal model, tailored to the challenges of multichannel source separation. The performance of this FPGA-based approach is comprehensively evaluated, taking advantage of the flexibility and computational capabilities of FPGAs to create an efficient real-time source separation framework. Our experimental results demonstrate consistent, high-quality results in terms of sound separation.

Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson's Disease: A Study on Speaker Diarization and Classification Techniques.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. One of the notable non-motor symptoms of PD is the presence of vocal disorders, attributed to the underlying pathophysiological changes in the neural control of the laryngeal and vocal tract musculature. From this perspective, the integration of machine learning (ML) techniques in the analysis of speech signals has significantly contributed to the detection and diagnosis of PD. Particularly, MEL Frequency Cepstral Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GTCCs) are both feature extraction techniques commonly used in the field of speech and audio signal processing that could exhibit great potential for vocal disorder identification. This study presents a novel approach to the early detection of PD through ML applied to speech analysis, leveraging both MFCCs and GTCCs. The recordings contained in the Mobile Device Voice Recordings at King's College London (MDVR-KCL) dataset were used. These recordings were collected from healthy individuals and PD patients while they read a passage and during a spontaneous conversation on the phone. Particularly, the speech data regarding the spontaneous dialogue task were processed through speaker diarization, a technique that partitions an audio stream into homogeneous segments according to speaker identity. The ML applied to MFCCS and GTCCs allowed us to classify PD patients with a test accuracy of 92.3%. This research further demonstrates the potential to employ mobile phones as a non-invasive, cost-effective tool for the early detection of PD, significantly improving patient prognosis and quality of life.

Audio signal processing technology in Arduino controlling

In recent years, the application of audio signal processing technology is frequent, which is beneficial for developing both technologies. A significant advantage of the application of audio signal processing is allowing users to input their commands by voice, which is why it is widely used in Arduino controlling. Besides, Arduino can also be applied in audio signal processing. Because Arduino is an open-source multi-functions platform with the features of low cost, its utilization will be wider and more flexible in audio-digital signal processing and many other fields in the following time. So, the combination of these two technologies can produce more and more meaningful outcomes. This paper first introduces the technical features and application scenarios of these two technologies respectively, and then analyzes some samples of the combination of the two technologies specifically and summarizes them in different aspects to know about the current situation of these fields. By deeply understanding the inadequacies of these projects, this paper also analyzes the future developing trends of the fields that can solve the current problems and improve the quality of the projects.

Research on sound signal filtering and processing delay based on multiple receivers

With the rapid development of modern communication and audio technology, sound signal processing has become more and more important. This paper deeply explores the potential of multi-receiver audio signal processing technology based on practical application scenarios, and studies the effect of beamforming technology and adaptive noise cancellation algorithm on audio signal quality improvement. Experimental results show that with proper technology selection and framework design, the audio signal processing effect in complex environments can be significantly improved. In addition, this paper also predicts the trend of signal filtering and processing in the future and puts forward suggestions for the application of deep learning in this field, the research of adaptive algorithms, the fusion of multi-sensor information, the optimization of computational efficiency, and the establishment of real scene simulation. Overall, sound signal processing has great potential and opportunities in practical applications, which are worthy of further research and exploration.

Software-architectural configuration of the multifunctional audio digital signal processor module for signal mediatesting of audio devices

Objectives. The aim of this study is to develop and analyze parameters for a multifunctional audio module based on the ADAU1701 audio digital signal processor in the SigmaStudio environment. This will be used for testing audio devices in the following modes: routing of balanced and unbalanced audio channels according to the differential scheme Di-Box/R Di-Box; spatiotemporal and dynamic audio processing; three-band monochannel cross-separation with independent equalization; and correction of the frequency response of the audio channel with tracking notch auto-suppression of electro-acoustic positive feedback in a given spectral band.Methods. Visual-graphical architectural programming of audio modules in the SigmaStudio and Flowstone, as well as algorithms for real-time signal audio measurements and analysis of experimental data in the REW and Soundcard Oscilloscope are used.Results. The characteristics of the Di-Box/R Di-Box circuit were studied, in order to estimate the effect of differential signal conversion on the signal-to-noise ratio in the audio signal path. The characteristics of the reverberation and saturation submodules were established. Furthermore, the effect of equalization modes on the frequency response correction of a studio audio monitor was determined. The paper also studied the effect of an audio compressor on the dynamic range and the level of the output signal. The experimental results of the submodule for compensating the frequency response of an audio monitor using matched filtering were established, and the spectral characteristics of the submodule for automatic suppression of electro-acoustic positive feedback were obtained.Conclusions. The software architecture of a multifunctional audio module based on the ADAU1701 audio digital signal processor for testing and debugging media devices in a given spectral-dynamic and spectral-temporal ranges was designed. Balanced routing allows the effect of noise induced into the audio channel to be reduced 20-fold, thus enabling calibration of pickup audio devices. The audio signal processing submodule provides: compression response in the dynamic range from −27 to 18.6 dB with the possibility of equalization parameterization in the range of 0.04–18 kHz; reverberation response in the range from 0.5–3000 ms; audio-channel cross-division into 3 with the ability to adjust the amplitude-frequency response in the dynamic range from −30 to 30 dB. The auto-correction submodule of the amplitude-frequency response allows the dynamic nonuniformity of the amplitude-frequency response to be reduced by 40 dB. The auto-suppression submodule of electro-acoustic positive feedback provides notch formant suppression up to −100 dB with an input dynamic range from −50 to 80 dB.

Analyzing the Influence of Diverse Background Noises on Voice Transmission: A Deep Learning Approach to Noise Suppression

This paper presents an approach to enhancing the clarity and intelligibility of speech in digital communications compromised by various background noises. Utilizing deep learning techniques, specifically a Variational Autoencoder (VAE) with 2D convolutional filters, we aim to suppress background noise in audio signals. Our method focuses on four simulated environmental noise scenarios: storms, wind, traffic, and aircraft. The training dataset has been obtained from public sources (TED-LIUM 3 dataset, which includes audio recordings from the popular TED-TALK series) combined with these background noises. The audio signals were transformed into 2D power spectrograms, upon which our VAE model was trained to filter out the noise and reconstruct clean audio. Our results demonstrate that the model outperforms existing state-of-the-art solutions in noise suppression. Although differences in noise types were observed, it was challenging to definitively conclude which background noise most adversely affects speech quality. The results have been assessed with objective (mathematical metrics) and subjective (listening to a set of audios by humans) methods. Notably, wind noise showed the smallest deviation between the noisy and cleaned audio, perceived subjectively as the most improved scenario. Future work should involve refining the phase calculation of the cleaned audio and creating a more balanced dataset to minimize differences in audio quality across scenarios. Additionally, practical applications of the model in real-time streaming audio are envisaged. This research contributes significantly to the field of audio signal processing by offering a deep learning solution tailored to various noise conditions, enhancing digital communication quality.

A review of differentiable digital signal processing for music and speech synthesis

The term “differentiable digital signal processing” describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilitating their integration into neural networks. This article surveys the literature on differentiable audio signal processing, focusing on its use in music and speech synthesis. We catalogue applications to tasks including music performance rendering, sound matching, and voice transformation, discussing the motivations for and implications of the use of this methodology. This is accompanied by an overview of digital signal processing operations that have been implemented differentiably, which is further supported by a web book containing practical advice on differentiable synthesiser programming (https://intro2ddsp.github.io/). Finally, we highlight open challenges, including optimisation pathologies, robustness to real-world conditions, and design trade-offs, and discuss directions for future research.

Acoustic Sensors and Audio Signal Processing in Intelligent Transportation Systems: A Survey

Acoustic Sensors and Audio Signal Processing in Intelligent Transportation Systems: A Survey

VERBAL COMMUNICATION: NOISE REDUCTION AND CHARACTERISTICS ANALYSIS USING WAVELET AND THRESHOLD DERIVATIVE METHOD

Noise reduction is a very difficult task but it has been a subject of intense research papers in recent years. Audio signal processing system is often suffering from noise troubles. Noise will seriously affect the quality of audio and even lead to audio processing failure. Simulation and results are performed in MATLAB R2012. In order to extract the desired speech signal from its corrupted signal, speech processing and communication systems are applied for effective noise reduction techniques. To avoid this noise troubles and quality degrades, noise reduction and characteristic analysis using discrete wavelet and threshold derivative method are used which improves the signal quality and to achieve high signal to noise ratio (SNR)

Intelligent IOT Based Audio Signal Processing for Healthcare Applications

This research introduces a novel approach to intelligent IoT-based audio signal processing for healthcare applications. Leveraging advanced feature extraction techniques such as Mel-Frequency Cepstral Coefficients (MFCC) and Wavelet Transform, combined with sophisticated classification models like Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), the proposed method demonstrates superior performance in accurately classifying healthcare data. Through extensive experimentation and analysis, the method achieves high accuracy, precision, recall, and F1 score, while exhibiting robustness in discriminating between different classes and maintaining precision in classification, as evidenced by its high AUC-ROC and AUC-PR values. The ablation study provides insights into the significance of key components and parameters, offering guidance for further refinement and optimization of the method. Overall, the proposed method holds promise for revolutionizing healthcare management through proactive monitoring and intervention, leading to improved patient outcomes and healthcare delivery.

Exploring the Integration and Innovation Path of Chinese Traditional Music Culture in Popular Percussion Education in Colleges and Universities

Abstract This study explores the integration of Chinese traditional music culture with contemporary percussion education in higher education institutions. It delves into three primary areas: the rhythmic language inherent in Chinese national instrumental music, the acoustic properties of combined percussive instruments, and the distinctive features of percussion music compositions. In response to the evolving trends in popular percussion education at universities, we have designed a curriculum framework titled “Chinese Percussion,” which leverages the abundant percussive resources available. This framework includes a detailed examination of audio signal processing and the extraction of percussion rhythms. Furthermore, we address the significant challenges in teaching percussion at the collegiate level and propose using the music spectrum to direct instructional rhythms. Employing the creative environment of the music classroom as a methodological tool, this study assesses the atmosphere of creativity in college percussion classes before and after incorporating traditional Chinese music elements. With the exception of the sixth dimension (classroom conflict atmosphere), which did not show statistical significance, all other positive dimensions—challenge/involvement, freedom, trust/openness, reflective thinking time, joy, support for creativity, discussion, and adventurous spirit—demonstrated significant improvements. Specifically, the seventh and eighth dimensions showed increases of 0.4121 and 0.6459 points, respectively, in post-test scores. Incorporating Chinese musical elements into percussion teaching not only enhances the uniqueness of the educational experience but also stimulates interest and engagement, thereby fostering the advancement of music education.

From Intonation Adjustments to Synchronization of Heart Rate Variability: Singer Interaction in Traditional Georgian Vocal Music

This paper is concerned with how singers of Georgian traditional vocal music interact when singing together. Applying a variety of computational methods from audio signal processing and music information retrieval (MIR), we examine three existing corpora of (field) recordings for manifestations of a high degree of mutual coordination of the singers’ voices. We find numerous examples of harmonically controlled mutual intonation adjustments on both short and long time scales. Furthermore, we believe that the observed differences in melodic and harmonic scales can also be interpreted as (side) effects of the singers’ interaction with the possible goal of achieving harmonic togetherness (or consonance) on the time scale of individual (important) notes. In addition, together with the ensemble Khelkhvavi from Ozurgeti, we conducted an experiment demonstrating the synchronization of singers’ heartbeat rates during the performance of the Gurian song Chven Mshvidoba. The results of our analysis show that a variety of measurable signs of interaction between singers can be observed and documented in existing corpora of Georgian traditional vocal music. Our experience also shows that relevant information about the synchronization of the bodily functions of singers during performances can nowadays be obtained with reasonable technical and logistical effort even in a ‘real world’ framework, thereby allowing us to address questions related to the ‘ecological validity’ of this kind of measurement.

Retracted: Audio Signal Acquisition and Processing System Based on Model DSP Rapid Design

Retracted: Audio Signal Acquisition and Processing System Based on Model DSP Rapid Design

Encoder-decoder models in sequence-to-sequence learning: A survey of RNN and LSTM approaches

In today's information age, from natural language processing to audio signal processing, from time series analysis to machine translation, the application of sequence data involves various fields. Encoder-decoder models, as a powerful approach to sequence modeling, have attracted extensive attention and research. This review paper aims to explore encoder-decoder models, focusing on the principles and operational steps of recurrent neural networks (RNN) and long short-term memory (LSTM), aiming to provide researchers and practitioners with a deep understanding of the fundamentals and applications of these models Condition. Through the analysis and summary of relevant literature, this study reveals the advantages of RNN and LSTM in sequence data processing; RNN structure is simple and effective, can process sequence input and output of any length, and can capture the time dependence in sequence data. But there is a problem of gradient disappearance, which makes it difficult to deal with long-term dependencies. To solve this problem, the LSTM model introduces different gating mechanisms, which effectively solve the gradient problem while better capturing long-term dependencies. Through the review of the principles and applications of the two, it provides a useful reference for further research and application.