Adaptive Filtering

How does adaptive filtering work in the context of digital signal processing?

Adaptive filtering in digital signal processing works by continuously adjusting filter coefficients based on the input signal to minimize the error between the desired output and the actual output. This adjustment is done iteratively using algorithms such as the least mean squares (LMS) or recursive least squares (RLS) to adapt to changes in the input signal over time.

Applications of Digital Audio Signal Processing in Telecommunications

How does adaptive filtering work in the context of digital signal processing?

What are the key differences between adaptive filtering and traditional fixed filtering techniques?

The key differences between adaptive filtering and traditional fixed filtering techniques lie in their ability to adjust to changing input signals. While traditional fixed filters have static coefficients that do not change, adaptive filters can automatically update their coefficients based on the input signal, making them more versatile and suitable for applications where the signal characteristics may vary.

SPS BSI Webinar: MIR137 polygenic risk for schizophrenia and ephrin-regulated pathway: Role in brain morphology

Date: 31 May 2024 Time: 1:00 PM ET (New York Time) Presenter(s): Dr. Elisabetta C. del Re Meeting information: Meeting number: 2632 269 5821 Password: hPFwSbt7H36 (47397287 when dialing from a phone or video system) Join by phone: +1-415-655-0002 US Toll Access code: 263 226 95821 Join us Friday, May 31st, 2024, at 1:00 PM ET for an exciting virtual talk by Dr. Elisabetta C. del Re entitled: “MIR137 polygenic risk for schizophrenia and ephrin-regulated pathway: Role in brain morphology” as part of the activities of the Brain Space Initiative, co-sponsored by the Center for Translational Research in Neuroimaging and Data Science (TReNDS) and the Data Science Initiative, IEEE Signal Processing Society. Abstract MIR137 polygenic risk for schizophrenia and ephrin-regulated pathway: Role in brain morphology Background/Objective. Enlarged lateral ventricle (LV) volume and decreased volume in the corpus callosum (CC) are hallmarks of schizophrenia (SZ). We previously showed an inverse correlation between LV and CC volumes in SZ, with global functioning decreasing with increased LV volume. This study investigates the relationship between LV volume, CC abnormalities, and the microRNA MIR137 and its regulated genes in SZ, because of MIR137’s essential role in neurodevelopment. Results: Increased LV volumes and decreased CC central, mid-anterior, and mid-posterior volumes were observed in SZ probands. The MIR137-regulated ephrin pathway was significantly associated with CC:LV ratio, explaining a significant proportion (3.42 %) of CC:LV variance, and more than for LV and CC separately. Other pathways explained variance in either CC or LV, but not both. CC:LV ratio was also positively correlated with Global Assessment of Functioning, supporting previous subsample findings. SNP-based heritability estimates were higher for CC central:LV ratio (0.79) compared to CC or LV separately. Discussion: Our results indicate that the CC:LV ratio is highly heritable, influenced in part by variation in the MIR137-regulated ephrin pathway. Findings suggest that. Biography Elisabetta del Re is an Assistant Professor of Psychiatry at Harvard Medical School and Principal Investigator of NIMH funded research. She has multidisciplinary training in basic science, mental health, neuroimaging, including electrophysiology, and genetics. She holds a MA and PhD in Biochemistry and Experimental Pathology from Boston University; A MA in Mental Health from BGSP. Dr. del Re’s interest is in understanding psychosis and other serious mental illnesses, by looking at the genetics informing neural processes. Recommended Articles: Blokland, Gabriëlla Antonina Maria, et al. "MIR137 polygenic risk for schizophrenia and ephrin-regulated pathway: Role in lateral ventricles and corpus callosum volume." International Journal of Clinical and Health Psychology 24.2 (2024): 100458. (Link to Paper) Heller, Carina, et al. "Smaller subcortical volumes and enlarged lateral ventricles are associated with higher global functioning in young adults with 22q11. 2 deletion syndrome with prodromal symptoms of schizophrenia." Psychiatry Research 301 (2021): 113979. (Link to Paper)

Posted by on 2024-05-29

(ICME 2025) 2025 IEEE International Conference on Multimedia and Expo

Date: 30 June-4 July 2025 Location: Nantes, France Conference Paper Submission Deadline: TBD

Posted by on 2024-05-28

Distinguished Lecture: Prof. Woon-Seng Gan (Nanyang Technological University, Singapore)

Date:  7 June 2024 Chapter: Singapore Chapter Chapter Chair: Mong F. Horng Title: Augmented/Mixed Reality Audio for Hearables: Sensing, Control and Rendering

Posted by on 2024-05-21

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

Date: 4-5 November 2024 Chapter: Tunisia Chapter Chapter Chair: Maha Charfeddine Title: Generative AI

Posted by on 2024-05-21

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

Can adaptive filtering algorithms automatically adjust to changes in the input signal without manual intervention?

Yes, adaptive filtering algorithms can automatically adjust to changes in the input signal without manual intervention. By continuously updating filter coefficients based on the input signal, adaptive filters can adapt to variations in the signal characteristics, making them well-suited for real-time applications where the input signal may change dynamically.

Can adaptive filtering algorithms automatically adjust to changes in the input signal without manual intervention?

How do adaptive filters handle non-stationary signals in real-time applications?

Adaptive filters handle non-stationary signals in real-time applications by continuously updating their filter coefficients to track changes in the input signal. This adaptability allows adaptive filters to effectively process signals with time-varying characteristics, making them ideal for applications where the signal may be non-stationary or unpredictable.

What are some common applications of adaptive filtering in audio and speech processing?

Common applications of adaptive filtering in audio and speech processing include noise cancellation, echo suppression, adaptive beamforming, and acoustic feedback cancellation. Adaptive filters are used to enhance speech quality, remove unwanted noise, and improve the overall audio experience in various communication systems and devices.

Speech Enhancement Systems

What are some common applications of adaptive filtering in audio and speech processing?
How do adaptive filters adapt to minimize the error between the desired output and the actual output?

Adaptive filters adapt to minimize the error between the desired output and the actual output by adjusting their filter coefficients iteratively. This adjustment is done based on an error signal that represents the difference between the desired output and the actual output, with the goal of minimizing this error over time through continuous adaptation.

What are the advantages of using adaptive filtering in noise cancellation and echo suppression systems?

The advantages of using adaptive filtering in noise cancellation and echo suppression systems include improved signal quality, enhanced speech intelligibility, and better overall performance in noisy environments. Adaptive filters can effectively suppress unwanted noise and echoes by continuously adapting to the changing acoustic environment, making them valuable tools for improving audio communication systems.

What are the advantages of using adaptive filtering in noise cancellation and echo suppression systems?

VoIP codec optimization plays a crucial role in enhancing call quality in telecommunications by efficiently compressing and decompressing audio data transmitted over the internet. By selecting the most suitable codec based on factors such as bandwidth availability, network conditions, and device capabilities, VoIP systems can deliver clearer voice communication with minimal latency, packet loss, and jitter. This optimization process involves adjusting parameters like bit rate, sample rate, and compression algorithms to ensure optimal audio quality while conserving bandwidth resources. Additionally, implementing advanced codecs like G.711, G.729, or Opus can further improve call quality by reducing background noise, echo, and distortion, resulting in a more seamless and immersive communication experience for users. Overall, VoIP codec optimization is essential for maximizing the efficiency and effectiveness of telecommunications services by prioritizing call quality and user satisfaction.

Digital audio signal processing plays a crucial role in emergency communication systems by enhancing the quality, clarity, and intelligibility of audio signals transmitted during critical situations. By utilizing advanced algorithms and techniques such as noise reduction, echo cancellation, and equalization, digital audio signal processing helps to ensure that emergency messages are effectively communicated to recipients in various environments. Additionally, digital audio signal processing enables the integration of features like automatic gain control and audio compression, which optimize the transmission of audio signals over different communication channels. Overall, digital audio signal processing plays a vital role in improving the overall performance and reliability of emergency communication systems, ultimately helping to save lives and mitigate risks during emergencies.

Digital signal modulation is utilized in audio transmission over telecommunication networks to convert analog audio signals into digital data for efficient transmission and reception. This process involves encoding the audio signal into a digital format using modulation techniques such as amplitude modulation (AM), frequency modulation (FM), or phase modulation (PM). These modulated signals are then transmitted over telecommunication networks, where they can be decoded back into analog audio signals at the receiving end. By using digital signal modulation, audio data can be transmitted with higher fidelity, reduced noise, and improved signal-to-noise ratio, ensuring clear and high-quality audio transmission over long distances in telecommunication networks. Additionally, digital modulation allows for the multiplexing of multiple audio signals onto a single transmission channel, increasing the efficiency of audio transmission over telecommunication networks.

Acoustic modeling plays a crucial role in telecommunication applications by enhancing speech recognition accuracy, improving noise cancellation capabilities, and enabling better voice quality in communication systems. By accurately capturing the acoustic characteristics of speech signals, acoustic modeling helps in distinguishing between different phonemes and words, leading to more precise transcription and interpretation of spoken language. This technology also aids in reducing background noise interference, allowing for clearer and more intelligible communication in noisy environments. Additionally, acoustic modeling enables the development of advanced voice-controlled interfaces and voice-activated devices, enhancing user experience and accessibility in telecommunication services. Overall, the benefits of acoustic modeling in telecommunication applications are vast and contribute to the efficiency and effectiveness of communication systems.

Binaural audio processing is utilized in telecommunication applications to create a more immersive and realistic listening experience for users. By capturing sound with two microphones placed at a distance similar to that of human ears, binaural processing can accurately reproduce spatial cues and directionality in audio signals. This technology enhances the perception of sound localization, making it easier for users to distinguish between different sources of sound and improving overall audio quality. In telecommunication applications, binaural audio processing can be used in virtual meetings, conference calls, and online gaming to create a more natural and engaging listening environment. Additionally, binaural processing can help reduce background noise and improve speech intelligibility, leading to clearer communication between users.

Psychoacoustic modeling is utilized in telecommunications to enhance audio compression by taking into account the human auditory system's sensitivity to different frequencies and sound levels. By analyzing the characteristics of audio signals and determining which components are less perceptible to the human ear, psychoacoustic models can efficiently remove or reduce these components during the compression process. This allows for the preservation of audio quality while reducing file sizes and bandwidth requirements. Through the use of advanced algorithms and encoding techniques based on psychoacoustic principles, telecommunications systems can deliver high-quality audio with minimal data usage, making communication more efficient and effective.

The requirements for real-time audio processing in telecommunications include low latency, high bandwidth, efficient data compression algorithms, robust error correction mechanisms, and reliable network connectivity. In order to achieve real-time audio processing, telecommunications systems must be equipped with advanced signal processing techniques, such as echo cancellation, noise reduction, and dynamic range compression. Additionally, the use of quality of service (QoS) protocols, such as RSVP and DiffServ, is essential to prioritize audio data packets and ensure a consistent level of service. Furthermore, real-time audio processing in telecommunications often requires the use of specialized hardware, such as digital signal processors (DSPs) and dedicated audio codecs, to handle the processing demands in a timely manner. Overall, the successful implementation of real-time audio processing in telecommunications relies on a combination of hardware, software, and network infrastructure that is specifically designed to meet the unique requirements of audio data transmission.