Acoustic Echo Control

How does acoustic echo control work in teleconferencing systems?

Acoustic echo control in teleconferencing systems works by detecting and removing any echo that occurs during a call. This is achieved by using algorithms that analyze the incoming audio signal and compare it to the outgoing signal. When a match is found, the system applies a filter to cancel out the echo, ensuring clear communication between all parties involved in the call.

Applications of Digital Audio Signal Processing in Telecommunications

How does acoustic echo control work in teleconferencing systems?

What are the key components of an acoustic echo control system?

The key components of an acoustic echo control system include a microphone, a speaker, a digital signal processor (DSP), and echo cancellation algorithms. The microphone picks up the incoming audio, which is then processed by the DSP to detect any echo. The echo cancellation algorithms work to remove the echo by generating an anti-noise signal that is played through the speaker, effectively canceling out the unwanted sound.

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

Distinguished Lecture: Dr. Tran Quoc Long (VNU University of Engineering and Technology, Vietnam)

Date: 16 May 2024 Chapter: Vietnam Chapter Chapter Chair: Nguyen Linh-Trung Title: How healthcare systems in Vietnam work?

Posted by on 2024-05-15

Distinguished Lecture: Prof. Maarten de Vos (KU Leuven, Belgium),

Date: 16 May 2024 Chapter: Vietnam Chapter Chapter Chair: Nguyen Linh-Trung Title: AI in healthcare: turning the hype into a help

Posted by on 2024-05-15

How does acoustic echo control differ from noise cancellation technology?

Acoustic echo control differs from noise cancellation technology in that it specifically targets and removes echo, while noise cancellation focuses on reducing background noise. While both technologies aim to improve audio quality, they serve different purposes and are often used in conjunction to create a more immersive and clear audio experience in teleconferencing systems.

How does acoustic echo control differ from noise cancellation technology?

Can acoustic echo control be used in conjunction with other audio processing technologies?

Acoustic echo control can be used in conjunction with other audio processing technologies such as noise reduction, automatic gain control, and equalization. By combining these technologies, teleconferencing systems can deliver high-quality audio with minimal interference, ensuring that all participants can hear and communicate effectively during calls.

What are the common challenges faced when implementing acoustic echo control in large conference rooms?

Common challenges faced when implementing acoustic echo control in large conference rooms include dealing with reverberation, managing multiple microphones and speakers, and ensuring that the system can adapt to changes in the room environment. These challenges require advanced algorithms and signal processing techniques to effectively cancel out echo and provide clear audio for all participants.

What are the common challenges faced when implementing acoustic echo control in large conference rooms?
How does acoustic echo control impact the overall audio quality in video conferencing applications?

Acoustic echo control plays a crucial role in video conferencing applications by ensuring that the audio remains clear and free from any unwanted echoes. By implementing effective echo control algorithms, video conferencing systems can provide a seamless communication experience, allowing participants to focus on the content of the meeting without being distracted by audio issues.

Are there different types of acoustic echo control algorithms available in the market?

There are different types of acoustic echo control algorithms available in the market, including acoustic echo cancellation (AEC), acoustic echo suppression (AES), and acoustic echo reduction (AER). Each algorithm has its own strengths and weaknesses, and the choice of which one to use depends on the specific requirements of the teleconferencing system. These algorithms work together to eliminate echo and provide a high-quality audio experience for all participants.

Digital Signal Modulation

Are there different types of acoustic echo control algorithms available in the market?

Low-latency audio streaming in live communication is maintained through a combination of optimized network protocols, efficient encoding and decoding algorithms, and real-time data processing techniques. By utilizing protocols such as Real-Time Transport Protocol (RTP) and User Datagram Protocol (UDP), audio data can be transmitted quickly and reliably over the network. Additionally, advanced audio codecs like Advanced Audio Coding (AAC) and Opus are used to compress and decompress audio data efficiently without sacrificing quality. Real-time data processing methods, such as jitter buffers and packet loss concealment, help mitigate delays and ensure smooth audio playback. Overall, the seamless integration of these technologies and techniques enables low-latency audio streaming in live communication scenarios.

Cross-talk suppression in telecommunication networks is achieved through various strategies such as frequency division multiplexing, time division multiplexing, spatial isolation, and signal processing techniques. Frequency division multiplexing separates signals into different frequency bands to prevent interference, while time division multiplexing allocates specific time slots for each signal to avoid overlap. Spatial isolation involves physically separating transmission lines to minimize cross-talk, and signal processing techniques like adaptive filtering and equalization help to enhance signal quality and reduce interference. By implementing these strategies, telecommunication networks can effectively suppress cross-talk and ensure reliable communication.

Echo suppressors and echo cancellers are both used in telecommunication systems to reduce or eliminate echo, but they differ in their methods of achieving this goal. Echo suppressors work by detecting and suppressing the echo signal in the communication channel, typically by reducing the volume of the signal during periods of silence. On the other hand, echo cancellers use sophisticated algorithms to actively cancel out the echo by generating an anti-phase signal to counteract the reflected sound. While both technologies aim to improve call quality by reducing echo, echo cancellers are generally more effective at completely eliminating echo compared to echo suppressors. Additionally, echo cancellers are more complex and expensive to implement than echo suppressors.

Digital audio signal processing is integral to the functionality of modern hearing aid technology. By utilizing algorithms and filters to manipulate sound waves, hearing aids can amplify specific frequencies, reduce background noise, and enhance speech clarity for individuals with hearing loss. This technology allows for customization based on the user's unique hearing profile, ensuring optimal performance in various listening environments. Additionally, digital signal processing enables features such as feedback cancellation, directionality, and wireless connectivity, enhancing the overall user experience. Overall, digital audio signal processing plays a crucial role in improving the effectiveness and versatility of hearing aids for individuals with hearing impairments.

Digital audio signal processing plays a crucial role in the advancement of voice-controlled systems by enhancing the accuracy and efficiency of speech recognition algorithms. By utilizing techniques such as noise reduction, echo cancellation, and voice activity detection, digital audio signal processing helps to improve the quality of audio input, making it easier for the system to accurately interpret and understand spoken commands. Additionally, features like beamforming and acoustic modeling can further optimize the performance of voice-controlled systems by isolating the user's voice and adapting to different acoustic environments. Overall, digital audio signal processing contributes to the development of more reliable and responsive voice-controlled systems that offer a seamless user experience.