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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
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The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
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The Discrete Fourier Transform (DFT) is a crucial tool for analyzing the frequency content of discrete-time signals. It converts a sequence of N samples from the time domain into its corresponding sequence in the frequency domain, where each sample represents a specific frequency component.
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The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
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Related Experiment Video

Updated: Mar 11, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Detection of fricatives using S-transform.

Hari Krishna Vydana1, Anil Kumar Vuppala1

  • 1International Institute of Information Technology, Hyderabad, India.

The Journal of the Acoustical Society of America
|December 3, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces an S-transform method for detecting fricatives in speech, improving accuracy over traditional methods. Combining it with a predictability measure enhances fricative boundary detection and duration extraction.

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Area of Science:

  • Speech processing
  • Acoustic phonetics
  • Signal analysis

Background:

  • Fricatives are characterized by high-frequency spectral energy and noise.
  • Existing spectral domain methods for fricative detection capture energy distribution.
  • Time-frequency representations are crucial for analyzing speech signals.

Purpose of the Study:

  • To explore the S-transform for detecting fricatives in continuous speech.
  • To compare the S-transform approach with the short-time Fourier transform (STFT) and predictability measure.
  • To develop a combined approach for improved fricative detection and boundary identification.

Main Methods:

  • Utilizing S-transform based time-frequency representation for spectral analysis.
  • Computing spectral evidence from S-transform and comparing it with STFT.
  • Applying a predictability measure to capture the noisy nature of fricatives.
  • Conducting a phone-level comparative analysis of S-transform and predictability measures.

Main Results:

  • S-transform based time-frequency representation offers progressive resolution, aiding in localizing high-frequency events.
  • Spectral evidence from S-transform outperformed that from STFT.
  • The S-transform and predictability measure approaches showed complementary phone distributions.
  • The combined approach demonstrated superior accuracy in detecting fricative boundaries and durations.

Conclusions:

  • The S-transform is effective for fricative detection and boundary identification in continuous speech.
  • Combining S-transform with predictability measures enhances fricative detection performance.
  • The proposed methods improve the extraction of durational information for fricatives.