Audio Signal De-Noising

How does wavelet thresholding help in reducing noise in audio signals?

Wavelet thresholding is a technique used to reduce noise in audio signals by decomposing the signal into different frequency bands and then applying a threshold to remove or reduce the noise in each band. By selectively removing noise at different scales, wavelet thresholding can effectively clean up audio signals without significantly distorting the original signal. This method is particularly useful for audio signals with non-stationary noise or varying levels of noise across different frequency ranges.

Audio Feature Extraction

How does wavelet thresholding help in reducing noise in audio signals?

What are the advantages of using spectral subtraction for audio signal de-noising?

Spectral subtraction is advantageous for audio signal de-noising as it works by estimating the noise spectrum and subtracting it from the noisy signal in the frequency domain. This method can effectively reduce background noise without affecting the desired signal, making it a popular choice for real-time noise reduction applications. Spectral subtraction is especially useful in scenarios where the noise characteristics are known or can be estimated accurately, allowing for precise noise removal while preserving the integrity of the audio signal.

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

Can adaptive filtering techniques effectively remove noise from audio signals?

Adaptive filtering techniques can be effective in removing noise from audio signals by continuously adjusting filter coefficients based on the input signal and the noise characteristics. By adapting to changes in the noise environment, adaptive filters can provide better noise reduction performance compared to fixed filters. These techniques are particularly useful in scenarios where the noise characteristics are time-varying or unknown, allowing for dynamic noise cancellation in audio signals.

Can adaptive filtering techniques effectively remove noise from audio signals?

How does the Wiener filter algorithm work in the context of audio signal de-noising?

The Wiener filter algorithm works in the context of audio signal de-noising by estimating the power spectral density of the desired signal and the noise, and then applying a frequency-dependent filter to minimize the mean square error between the estimated signal and the noisy signal. By leveraging statistical properties of the signal and noise, the Wiener filter can effectively reduce noise while preserving the desired signal components in audio signals. This method is particularly useful for stationary noise environments where the noise characteristics are known.

What role does non-linear filtering play in enhancing the quality of audio signals by reducing noise?

Non-linear filtering plays a crucial role in enhancing the quality of audio signals by reducing noise through techniques such as median filtering, nonlinear diffusion filtering, and morphological filtering. These methods are effective in removing impulsive noise, preserving signal edges, and reducing noise while preserving signal details. Non-linear filtering can be particularly useful in scenarios where traditional linear filters may not be effective in removing certain types of noise artifacts in audio signals.

What role does non-linear filtering play in enhancing the quality of audio signals by reducing noise?
How do time-frequency domain methods like Short-Time Fourier Transform (STFT) contribute to de-noising audio signals?

Time-frequency domain methods like Short-Time Fourier Transform (STFT) contribute to de-noising audio signals by providing a time-varying representation of the signal in the frequency domain. By analyzing the signal's spectrogram over short time intervals, STFT can help identify and remove noise components that vary over time. This method is particularly useful for audio signals with time-varying noise or transient noise events, allowing for effective noise reduction while preserving the temporal characteristics of the signal.

Applications of Digital Audio Signal Processing in Telecommunications

What are the limitations of using simple filtering techniques for audio signal de-noising compared to more advanced methods like deep learning algorithms?

Simple filtering techniques for audio signal de-noising have limitations compared to more advanced methods like deep learning algorithms, as they may not be able to effectively remove complex noise patterns or preserve signal details. Deep learning algorithms, on the other hand, can learn complex noise patterns and signal characteristics from large datasets, allowing for more accurate noise reduction and signal enhancement. While simple filtering techniques may be sufficient for basic noise reduction tasks, advanced methods like deep learning can provide superior performance in challenging audio signal de-noising scenarios.

What are the limitations of using simple filtering techniques for audio signal de-noising compared to more advanced methods like deep learning algorithms?

Implementing high-fidelity audio in low-bandwidth networks poses several challenges that must be addressed for optimal performance. One major issue is the potential for data loss or degradation during transmission, leading to a decrease in audio quality. This can be exacerbated by network congestion, latency, and packet loss, all of which can impact the overall listening experience. Additionally, the need for efficient compression algorithms and adaptive streaming techniques is crucial to ensure that audio files can be transmitted and played back smoothly without sacrificing quality. Furthermore, the limited bandwidth available in low-bandwidth networks may require trade-offs between audio quality and network performance, making it essential to find a balance that meets the needs of users while maintaining a reliable connection. Overall, implementing high-fidelity audio in low-bandwidth networks requires careful consideration of various technical factors to overcome these challenges and deliver a satisfactory listening experience.

Voice activity detection (VAD) plays a crucial role in enhancing call quality in noisy environments by accurately identifying and distinguishing between speech and background noise. By utilizing advanced algorithms and signal processing techniques, VAD can effectively suppress unwanted noise, echo, and interference, ensuring that only clear and intelligible speech is transmitted during a call. This results in improved audio quality, reduced distortion, and enhanced overall communication experience for users, especially in challenging acoustic conditions. Additionally, VAD helps optimize bandwidth usage by minimizing the transmission of unnecessary noise, leading to more efficient and reliable communication in noisy environments. Overall, the implementation of VAD technology significantly contributes to enhancing call quality and ensuring seamless communication even in adverse acoustic environments.

Acoustic echo control in hands-free communication devices is achieved through the implementation of advanced algorithms that analyze incoming audio signals and remove any unwanted echoes caused by feedback loops. These devices utilize adaptive filters, echo cancellers, and noise reduction techniques to ensure clear and crisp audio quality during calls. By continuously monitoring and adjusting the audio signals in real-time, acoustic echo control systems can effectively suppress echoes and prevent any disruptions in communication. Additionally, the use of acoustic modeling and echo suppression algorithms further enhances the performance of these devices in various environments, such as noisy or reverberant spaces. Overall, the integration of sophisticated signal processing technologies plays a crucial role in achieving optimal acoustic echo control in hands-free communication devices.

Spatial audio processing is utilized in virtual teleconferencing to create a more immersive and realistic audio experience for participants. By incorporating techniques such as binaural audio, sound localization, and acoustic modeling, virtual teleconferencing platforms can simulate the sensation of sound coming from different directions and distances, mimicking the way sound behaves in the real world. This helps to enhance the sense of presence and engagement during virtual meetings, making it easier for participants to identify who is speaking and where they are located within the virtual space. Additionally, spatial audio processing can also help reduce auditory fatigue and improve overall communication clarity by creating a more natural and dynamic audio environment.

Recent advancements in audio signal processing for satellite communications have focused on improving efficiency, reliability, and quality of transmission. One key development is the integration of adaptive algorithms to enhance noise reduction and echo cancellation, ensuring clear audio reception even in challenging environments. Additionally, the implementation of advanced modulation techniques such as quadrature amplitude modulation (QAM) and phase-shift keying (PSK) has enabled higher data rates and improved spectral efficiency. Furthermore, the use of software-defined radios (SDRs) allows for greater flexibility in signal processing and modulation schemes, leading to more robust communication systems. Overall, these innovations in audio signal processing are paving the way for enhanced satellite communication capabilities in various industries such as broadcasting, telecommunication, and remote sensing.