Audio Feature Extraction

How does audio feature extraction help in music genre classification?

Audio feature extraction plays a crucial role in music genre classification by capturing key characteristics of audio signals that can differentiate between different genres. Features such as spectral centroid, zero-crossing rate, and Mel-frequency cepstral coefficients (MFCCs) are commonly extracted to represent the timbral and rhythmic aspects of music. These features provide valuable information for machine learning algorithms to classify music into specific genres based on their unique acoustic properties.

Applications of Digital Audio Signal Processing in Telecommunications

Dynamic Range Control

How does audio feature extraction help in music genre classification?

What are the key audio features extracted for speech emotion recognition?

In speech emotion recognition, key audio features extracted include pitch, energy, formants, and prosodic features. These features help in capturing the emotional content of speech signals by analyzing variations in pitch, intensity, and spectral characteristics. By extracting these features, machine learning models can be trained to accurately classify different emotional states expressed in speech, such as happiness, sadness, anger, or neutrality.

Call for Proposals: IEEE MLSP 2026

Submission Deadline: 15 August 2024 IEEE Signal Processing Society’s Machine Learning for Signal Processing Technical Committee (MLSP TC) is soliciting proposals from researchers interested in organizing the 2026 MLSP Workshop. The MLSP Workshop is a four-day workshop and will include tutorials on the first day. Proposing teams are asked to create a proposal that follows the following outline: Location and Venue: Give an idea on the venue size and facilities. Conference Dates: Ensure they do not conflict with major holidays, or other SPS conferences and workshops. Typically, the workshop is held during the period of mid-September to mid-October. Organizing Committee Members: Build the organizing committee considering factors including (a) active SPS members; (b) diversity in geographical, industry and academia, age, and gender; (c) conference and/or workshop experience; (d) event management experience. For examples, refer to the MLSP Workshops page. Technical Program: Consider the overall structure and conference model; innovative initiatives; student and young professional initiatives; and industry-participation/support initiatives. Budget including registration fees. Hotels in the area that cater to different attendee budget levels. Travel and transportation between the nearest airport and the conference venue. Any other relevant information about the venue or the organization. The intention letter deadline is August 1, 2024, and the deadline for submission of proposals is August 15, 2024. Please submit your proposal to the MLSP TC Chair, Wenwu Wang, and the MLSP Workshop Sub-Committee Chair, Roland Hostettler, via email. We encourage you to contact them with questions or to obtain further details about the content of the proposals. Proposals will be reviewed by the MLSP TC, and the selection results will be announced in October 2024.  

Posted by on 2024-05-21

Two Post Doctoral Researchers and One PhD Student in Advanced Medical Image Analysis

Project Description We are glad to announce the launch of a new research project based on the collaboration between the Mathematics and Data Science (MADS) research group at Vrije Universiteit Brussel (VUB) and the Centre for Reproductive Medicine at UZ Brussel (Brussels IVF). This project aims at helping the field of assisted reproductive technology (ART) by developing innovative AI-driven frameworks for the analysis of high-dimensional oocyte/embryo images. By integrating advanced deep learning and mathematical modeling, we seek to investigate, understand and potentially improve decision-making in ART procedures. The ultimate objective of this interdisciplinary research is to push the boundaries of current reproductive treatment, potentially offering new insights and tools for clinicians. Open Positions We are opening the following research positions in Digital Mathematics (DIMA), a research group chaired by Prof. Ann Dooms from MADS, VUB. 1. Post-doctoral Researchers (2 vacancies) Focus Area: Advanced deep learning and machine intelligence for medical image analysis. Duration: Full-time position for 2 years (with possibility for extending to 30 month). Starting from 1stSeptember 2024. Key Responsibilities: Conceptualize, develop and implement deep learning and mathematical modeling algorithms for analyzing high-dimensional medical images. Collaborate with embryologists and clinicians to integrate biological motivations into AI models. Publish research findings in high-impact journals and present at conferences. Requirements: PhD in Applied Mathematics, Computer Science, Electrical/Electronic/Information Engineering, or related fields. Strong background in deep learning, machine learning, computer vision and image processing. Proven track record of publications in top-tier conferences and journals. Excellent programming skills in Python/MATLAB and rich experiences with deep learning frameworks (e.g., PyTorch). English as official working language.  2. Doctoral Candidate (1 position) Focus Area: Mathematical modeling and machine learning for image analysis. Duration: Full-time for 3 years (with possibility for extending to 4 years). Starting from 1st August 2024.  Key Responsibilities: Develop mathematical models to assist/enhance AI-driven (e.g., deep learning based) image analysis. Work closely with embryologists and post-doctoral researchers to integrate these models into the overall framework. Data collection, preprocessing, and annotation. Contribute to writing research papers and project reports. Obtain a PhD diploma following the regulations of VUB. Requirements: Master's degree in (Applied) Mathematics, Computer Science, Electronic and Information Engineering, or related fields. Strong analytical and problem-solving skills, being able to conduct independent research and development with strong self-motivation. Experiences with mathematical modeling, machine learning and computer vision. Proficiency in programming languages such as Python or MATLAB. English as official working language. How to Apply If you are a highly motivated individual with a passion for advancing medical technology through AI and mathematical modeling, we encourage you to apply. Please send your CV and a cover letter detailing your research experience and interests to Prof. Ann Dooms ([email protected]) and Prof. Tan Lu ([email protected]).  All applications must be sent before 1st July 2024.

Posted by on 2024-05-20

Two Post Doctoral Researchers and One PhD Student in Advanced Medical Image Analysis

Project Description We are glad to announce the launch of a new research project based on the collaboration between the Mathematics and Data Science (MADS) research group at Vrije Universiteit Brussel (VUB) and the Centre for Reproductive Medicine at UZ Brussel (Brussels IVF). This project aims at helping the field of assisted reproductive technology (ART) by developing innovative AI-driven frameworks for the analysis of high-dimensional oocyte/embryo images. By integrating advanced deep learning and mathematical modeling, we seek to investigate, understand and potentially improve decision-making in ART procedures. The ultimate objective of this interdisciplinary research is to push the boundaries of current reproductive treatment, potentially offering new insights and tools for clinicians. Open Positions We are opening the following research positions in Digital Mathematics (DIMA), a research group chaired by Prof. Ann Dooms from MADS, VUB. 1. Post-doctoral Researchers (2 vacancies) Focus Area: Advanced deep learning and machine intelligence for medical image analysis. Duration: Full-time position for 2 years (with possibility for extending to 30 month). Starting from 1stSeptember 2024. Key Responsibilities: Conceptualize, develop and implement deep learning and mathematical modeling algorithms for analyzing high-dimensional medical images. Collaborate with embryologists and clinicians to integrate biological motivations into AI models. Publish research findings in high-impact journals and present at conferences. Requirements: PhD in Applied Mathematics, Computer Science, Electrical/Electronic/Information Engineering, or related fields. Strong background in deep learning, machine learning, computer vision and image processing. Proven track record of publications in top-tier conferences and journals. Excellent programming skills in Python/MATLAB and rich experiences with deep learning frameworks (e.g., PyTorch). English as official working language.  2. Doctoral Candidate (1 position) Focus Area: Mathematical modeling and machine learning for image analysis. Duration: Full-time for 3 years (with possibility for extending to 4 years). Starting from 1st August 2024.  Key Responsibilities: Develop mathematical models to assist/enhance AI-driven (e.g., deep learning based) image analysis. Work closely with embryologists and post-doctoral researchers to integrate these models into the overall framework. Data collection, preprocessing, and annotation. Contribute to writing research papers and project reports. Obtain a PhD diploma following the regulations of VUB. Requirements: Master's degree in (Applied) Mathematics, Computer Science, Electronic and Information Engineering, or related fields. Strong analytical and problem-solving skills, being able to conduct independent research and development with strong self-motivation. Experiences with mathematical modeling, machine learning and computer vision. Proficiency in programming languages such as Python or MATLAB. English as official working language. How to Apply If you are a highly motivated individual with a passion for advancing medical technology through AI and mathematical modeling, we encourage you to apply. Please send your CV and a cover letter detailing your research experience and interests to Prof. Ann Dooms ([email protected]) and Prof. Tan Lu ([email protected]).  All applications must be sent before 1st July 2024.

Posted by on 2024-05-20

Distinguished Lecture: Prof. Dr. Justin Dauwels (TU Delft)

Date: 15 June 2024 Chapter: UAE Joint w/ComSoc Chapter Chapter Chair: Diana Wasfi Dawoud Title: TBA

Posted by on 2024-05-15

How are audio features like MFCCs used in speaker identification systems?

Audio features like MFCCs are commonly used in speaker identification systems to represent the unique vocal characteristics of individuals. By extracting MFCCs from speech signals, the system can capture the distinctive patterns in an individual's voice, such as pitch, timbre, and intonation. These features are then used to create speaker models for identifying and verifying speakers in various applications, such as security systems or voice-controlled devices.

How are audio features like MFCCs used in speaker identification systems?

Can audio feature extraction be used for detecting environmental sounds in urban areas?

Audio feature extraction can indeed be utilized for detecting environmental sounds in urban areas by capturing acoustic characteristics that are specific to different sound sources. Features such as spectral contrast, spectral flux, and temporal features can help in distinguishing between various environmental sounds, such as traffic noise, sirens, or construction activities. By extracting these features and training machine learning models, urban sound monitoring systems can effectively classify and detect different sound events in noisy urban environments.

What role do spectrogram features play in audio event detection systems?

Spectrogram features play a crucial role in audio event detection systems by providing a visual representation of the frequency content of audio signals over time. Spectrograms capture the spectral characteristics of sound events, allowing for the identification of specific patterns and structures in audio data. By analyzing spectrogram features using signal processing techniques, audio event detection systems can accurately detect and classify different sound events, such as footsteps, door slams, or glass breaking.

What role do spectrogram features play in audio event detection systems?
How do audio feature extraction techniques differ between music and speech processing applications?

Audio feature extraction techniques differ between music and speech processing applications due to the distinct characteristics of these audio signals. In music processing, features like tempo, rhythm, and harmony are more relevant for genre classification and music analysis. On the other hand, speech processing focuses on features related to pitch, formants, and prosody for tasks such as speech recognition and emotion detection. While there may be some overlap in certain features, the emphasis on different aspects of audio signals sets apart the feature extraction techniques used in music and speech processing.

What are the challenges in extracting audio features from low-quality recordings for audio analysis tasks?

Extracting audio features from low-quality recordings poses challenges for audio analysis tasks due to the presence of noise, distortion, and artifacts in the audio signals. In such cases, traditional feature extraction methods may not be as effective in capturing meaningful information from the audio data. Techniques such as noise reduction, signal enhancement, and feature normalization are often employed to improve the quality of audio features extracted from low-quality recordings. Additionally, advanced machine learning algorithms and signal processing methods can help in mitigating the impact of noise and artifacts on the accuracy of audio analysis tasks.

What are the challenges in extracting audio features from low-quality recordings for audio analysis tasks?

Voice Over LTE (VoLTE) utilizes digital audio signal processing by converting analog voice signals into digital data packets for transmission over LTE networks. This process involves encoding, decoding, compression, and decompression of audio signals to ensure high-quality voice communication. VoLTE also incorporates advanced audio processing techniques such as noise cancellation, echo suppression, and voice enhancement to improve call clarity and reduce background noise. By leveraging digital audio signal processing, VoLTE enables efficient voice transmission over LTE networks, delivering clear and reliable voice calls to users. Additionally, VoLTE supports features like HD voice and simultaneous voice and data transmission, enhancing the overall user experience.

Automatic gain control (AGC) plays a crucial role in maintaining consistent audio levels by adjusting the gain of an audio signal in real-time. This technology helps to prevent sudden spikes or drops in volume, ensuring a smooth and balanced listening experience for the audience. By continuously monitoring the input signal and making automatic adjustments, AGC helps to normalize the audio levels, keeping them within a desired range. This contributes to a more professional and polished sound quality, especially in situations where multiple audio sources are being used or when dealing with varying recording conditions. Overall, AGC helps to create a more consistent and enjoyable listening experience for the audience.

Lossless audio compression in telecommunications offers several benefits, including reduced bandwidth usage, improved sound quality, and efficient storage capabilities. By utilizing lossless compression algorithms such as FLAC or ALAC, telecommunications companies can transmit audio data without sacrificing any quality, ensuring that the original sound is preserved during transmission. This results in a more accurate reproduction of the audio signal at the receiving end, leading to a better overall listening experience for users. Additionally, lossless compression allows for more efficient use of network resources, as smaller file sizes require less bandwidth to transmit, reducing costs for both providers and consumers. Furthermore, the ability to store audio files in a compressed, lossless format enables telecommunications companies to save space on servers and devices while maintaining high-fidelity audio playback. Overall, the use of lossless audio compression in telecommunications helps to optimize network performance, enhance user experience, and streamline data storage processes.

Digital audio compression plays a crucial role in reducing bandwidth usage by utilizing algorithms to decrease the size of audio files without significantly compromising audio quality. By employing techniques such as lossy compression, perceptual coding, and bit rate reduction, digital audio files can be efficiently compressed to consume less data during transmission or storage. This reduction in file size allows for faster streaming and downloading speeds, as well as lower data consumption for users. Additionally, compressed audio files require less storage space, making them easier to manage and distribute. Overall, digital audio compression is essential for optimizing bandwidth usage and improving the efficiency of audio transmission over networks.