Speech Compression Algorithms

How does the Discrete Cosine Transform (DCT) algorithm contribute to speech compression?

The Discrete Cosine Transform (DCT) algorithm contributes to speech compression by converting a signal into its frequency components, allowing for the removal of redundant information and the retention of essential data. By transforming the speech signal into a set of cosine functions with different frequencies, the DCT algorithm helps in reducing the overall size of the speech data while maintaining its quality and intelligibility.

How does the Discrete Cosine Transform (DCT) algorithm contribute to speech compression?

Can you explain the difference between lossy and lossless speech compression algorithms?

Lossy speech compression algorithms discard some data during the compression process to achieve higher compression ratios, resulting in a loss of quality in the reconstructed speech signal. On the other hand, lossless speech compression algorithms aim to reduce the size of the speech data without any loss of information, making them suitable for applications where preserving the original speech quality is crucial.

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

What role does the Huffman coding algorithm play in reducing the size of speech data?

The Huffman coding algorithm plays a crucial role in reducing the size of speech data by assigning variable-length codes to different speech symbols based on their frequencies. By assigning shorter codes to more frequent symbols and longer codes to less frequent symbols, Huffman coding helps in efficiently encoding the speech data, leading to a reduction in the overall size of the compressed speech signal.

Applications of Digital Audio Signal Processing in Telecommunications

What role does the Huffman coding algorithm play in reducing the size of speech data?

How do adaptive differential pulse code modulation (ADPCM) algorithms work in speech compression?

Adaptive Differential Pulse Code Modulation (ADPCM) algorithms work in speech compression by predicting the next sample in the speech signal based on the previous samples and encoding the difference between the predicted and actual samples. By adapting the prediction based on the input signal, ADPCM algorithms can achieve higher compression ratios while maintaining the quality of the reconstructed speech signal.

What are the advantages of using the Modified Discrete Cosine Transform (MDCT) algorithm for speech compression?

The Modified Discrete Cosine Transform (MDCT) algorithm offers advantages for speech compression by dividing the speech signal into shorter overlapping blocks, allowing for better frequency resolution and reducing artifacts in the reconstructed speech signal. By using a windowing function to minimize spectral leakage, MDCT can achieve higher compression ratios with improved speech quality.

What are the advantages of using the Modified Discrete Cosine Transform (MDCT) algorithm for speech compression?
How does the use of vector quantization improve the efficiency of speech compression algorithms?

The use of vector quantization improves the efficiency of speech compression algorithms by grouping similar speech vectors into clusters and representing them with a single codeword. By quantizing the speech vectors into a smaller set of representative codewords, vector quantization reduces the amount of data needed to encode the speech signal, leading to higher compression ratios.

Can you discuss the impact of psychoacoustic models on speech compression algorithms?

Psychoacoustic models play a significant role in speech compression algorithms by taking into account the human auditory system's limitations and perceptual characteristics. By identifying and removing irrelevant or imperceptible components of the speech signal, psychoacoustic models help in reducing the overall size of the compressed speech data while maintaining the perceived quality of the reconstructed speech signal.

Applications of Digital Audio Signal Processing in Telecommunications

Can you discuss the impact of psychoacoustic models on speech compression algorithms?

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.

Speech compression algorithms differ from traditional audio compression techniques in several key ways. While traditional audio compression focuses on reducing the file size of music or other audio recordings by removing redundant or unnecessary data, speech compression algorithms specifically target the unique characteristics of human speech. These algorithms often utilize techniques such as phonetic analysis, voice recognition, and linguistic modeling to identify and compress speech patterns more effectively. Additionally, speech compression algorithms may prioritize preserving the clarity and intelligibility of speech over minimizing file size, as the primary goal is often to maintain the quality of the spoken content. Overall, speech compression algorithms are tailored to the specific requirements and nuances of human speech, setting them apart from more general audio compression methods.

Machine learning is increasingly being utilized to enhance digital audio signal processing in the telecom industry. By leveraging algorithms that can automatically learn and improve from data, telecom companies are able to optimize audio quality, reduce background noise, and enhance speech recognition capabilities. Through the use of neural networks, deep learning, and other advanced techniques, machine learning models can adapt to different audio environments, leading to more accurate and efficient processing of audio signals. This results in improved call quality, better customer experiences, and overall enhanced communication services in the telecom sector. Additionally, machine learning can help identify and mitigate issues such as echo, distortion, and latency in real-time, further improving the overall audio processing capabilities in telecom networks.

Audio watermarking in telecommunications has various applications that enhance security, copyright protection, and content authentication. By embedding imperceptible watermarks into audio signals, telecommunications companies can prevent unauthorized distribution of content, track the origin of leaked materials, and verify the authenticity of audio files. This technology is crucial for digital rights management, ensuring that intellectual property rights are upheld in the digital realm. Additionally, audio watermarking can be used for monitoring and tracking purposes, enabling telecommunications providers to detect illegal activities such as piracy and unauthorized sharing of copyrighted material. Overall, the applications of audio watermarking in telecommunications play a vital role in safeguarding the integrity and ownership of audio content in the digital age.