In audio processing, there are various types of noise reduction techniques employed to enhance the quality of the audio signal. Some common methods include spectral subtraction, adaptive filtering, wavelet denoising, and machine learning algorithms. These techniques aim to reduce unwanted noise present in the audio signal, improving clarity and overall sound quality.
Adaptive filtering plays a crucial role in noise reduction in signal processing by continuously adjusting filter coefficients based on the input signal. This adaptive nature allows the filter to adapt to changing noise characteristics, effectively reducing noise in real-time. By dynamically updating filter parameters, adaptive filtering can efficiently suppress noise while preserving the desired signal components.
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
Date: 30 June-4 July 2025 Location: Nantes, France Conference Paper Submission Deadline: TBD
Posted by on 2024-05-28
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
Date: 4-5 November 2024 Chapter: Tunisia Chapter Chapter Chair: Maha Charfeddine Title: Generative AI
Posted by on 2024-05-21
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
Spectral subtraction is a widely used technique in noise reduction that involves estimating the noise spectrum and subtracting it from the noisy signal spectrum. By subtracting the noise component from the signal, spectral subtraction effectively enhances the signal-to-noise ratio, resulting in cleaner audio output. This method is particularly useful in scenarios where the noise characteristics are relatively stationary.
Wavelet denoising is a powerful technique used in noise reduction applications to remove unwanted noise from audio signals. By decomposing the signal into different frequency components using wavelet transforms, wavelet denoising can effectively separate noise from the desired signal. This method is beneficial for preserving signal details while suppressing noise artifacts, leading to improved audio quality.
Machine learning algorithms, such as deep learning, have revolutionized noise reduction techniques by leveraging complex neural networks to learn noise patterns and suppress them effectively. Deep learning models can adapt to various noise types and levels, making them highly versatile in noise reduction applications. By training on large datasets, these algorithms can achieve superior noise reduction performance compared to traditional methods.
Time-frequency analysis plays a crucial role in noise reduction methods by providing insights into the time-varying characteristics of the signal and noise components. Techniques such as short-time Fourier transform and wavelet transforms enable the analysis of signal properties in both time and frequency domains, facilitating the identification and suppression of noise components. Time-frequency analysis is essential for designing effective noise reduction algorithms.
Blind source separation techniques are instrumental in separating noise from the desired signal in audio processing without prior knowledge of the source signals. By exploiting statistical properties and spatial characteristics of the input signals, blind source separation methods can effectively isolate noise components from the primary signal. This separation process enhances the overall audio quality by reducing unwanted noise interference, making it a valuable tool in noise reduction applications.
Applications of Digital Audio Signal Processing in Telecommunications
One technique for improving intelligibility of speech in noisy environments is utilizing noise-canceling technology, which helps to reduce background noise and enhance the clarity of the speaker's voice. Another effective method is employing directional microphones, which can pick up sound from a specific direction while minimizing surrounding noise. Additionally, speech enhancement algorithms can be used to filter out unwanted noise and emphasize the speech signal, making it easier to understand in challenging acoustic conditions. Furthermore, utilizing assistive listening devices such as FM systems or hearing aids with speech enhancement features can also improve speech intelligibility in noisy environments. Overall, a combination of these techniques can significantly enhance communication effectiveness in adverse listening situations.
Adaptive filtering is utilized in speech enhancement to improve the clarity of audio signals by adjusting filter coefficients in real-time based on the characteristics of the input signal. By analyzing the spectral content, noise levels, and other parameters of the speech signal, adaptive filters can effectively suppress background noise, reverberation, and other unwanted artifacts, thereby enhancing speech intelligibility. This process involves the use of algorithms such as least mean squares (LMS) and recursive least squares (RLS) to continuously update filter weights and minimize the error between the desired and actual signals. Through this adaptive approach, speech clarity can be significantly improved, making it easier for listeners to understand and interpret spoken words in noisy environments.
Advanced teleconferencing systems that utilize DSP (Digital Signal Processing) typically consist of several key components. These may include high-quality microphones with noise-cancellation technology, echo cancellation algorithms, audio mixers, audio codecs for compression and decompression of audio signals, and advanced DSP processors for real-time audio processing. Additionally, these systems may also incorporate high-definition cameras with pan-tilt-zoom functionality, video codecs for video compression, and decompression, as well as DSP algorithms for video enhancement and noise reduction. Other components may include network interfaces for seamless connectivity, user interfaces for easy control and management, and software applications for customization and integration with other devices. Overall, advanced teleconferencing systems that leverage DSP technology offer a comprehensive solution for high-quality audio and video communication in various settings.
Bandwidth-efficient audio transmission plays a crucial role in optimizing network performance by reducing the amount of data required to transmit audio signals. By utilizing compression algorithms, such as MP3 or AAC, the size of audio files can be significantly reduced without compromising audio quality. This results in faster transmission speeds, lower latency, and decreased network congestion. Additionally, technologies like adaptive bitrate streaming and packet loss concealment further enhance the efficiency of audio transmission over networks. Overall, implementing bandwidth-efficient audio transmission techniques can lead to improved network performance, better user experience, and more reliable audio communication.
Packet loss concealment in VoIP involves various techniques to mitigate the impact of lost packets on call quality. Some common methods include forward error correction (FEC), which adds redundant data to packets to enable receivers to reconstruct lost information, and interleaving, which spreads out data across multiple packets to reduce the impact of consecutive losses. Additionally, techniques such as jitter buffers, packet reordering, and packet duplication can help smooth out the effects of packet loss on voice calls. These strategies work together to improve the overall user experience by minimizing disruptions and ensuring clear communication during VoIP calls.
Audio signal processing plays a crucial role in enhancing customer service in call centers by improving the quality of incoming and outgoing calls through noise reduction, echo cancellation, and voice clarity. By utilizing advanced algorithms and technologies such as automatic speech recognition (ASR) and natural language processing (NLP), call centers can analyze customer interactions in real-time to provide personalized responses and solutions. This leads to increased customer satisfaction, reduced call handling times, and improved overall efficiency. Additionally, audio signal processing enables call centers to monitor agent performance, identify trends, and gather valuable insights for training and process improvement. Overall, the integration of audio signal processing in call centers significantly enhances the customer service experience and helps organizations deliver exceptional support to their clients.
Multichannel audio transmission in teleconferencing offers numerous benefits, including improved sound quality, enhanced spatial awareness, increased immersion, better noise cancellation, and superior overall audio performance. By utilizing multiple channels for audio transmission, teleconferencing systems can deliver a more realistic and lifelike audio experience, allowing participants to feel as though they are in the same room. This technology also enables clearer communication, reduced background noise, and a more engaging and productive meeting environment. Additionally, multichannel audio transmission can support various audio formats and configurations, catering to the diverse needs and preferences of users. Overall, the use of multichannel audio transmission in teleconferencing enhances the overall communication experience and contributes to more effective and efficient virtual meetings.