IP Library Granted Patent US 11,996,115
Granted Patent B2
US 11,996,115 · App. 17/435,761 · Granted May 28, 2024

Sound processing method

Inventor: Mitsuru Sendoda (Tokyo, JP)
Assignee: NEC CORPORATION
G10L25/24G10L25/51
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Quick Facts
Patent No.
US 11,996,115
App. No.
17/435,761
Granted
May 28, 2024
Kind
B2
Abstract

A sound processing apparatus includes a feature value extractor configured to perform a Fourier transform and then a cepstral analysis of a sound signal and to extract, as feature values of the sound signal, values including frequency components obtained by the Fourier transform of the sound signal and a value based on a result obtained by the cepstral analysis of the sound signal.

Claims (27)

1. A sound processing method performed by a computer and comprising:

performing a Fourier transform and then a cepstral analysis of a sound signal; and

extracting, as feature values of the sound signal, values including frequency components obtained by the Fourier transform of the sound signal, a zero-th-order component of a result obtained by the cepstral analysis of the sound signal, and a differential component of the result obtained by the cepstral analysis of the sound signal.

2. The sound processing method according to claim 1 , wherein the cepstral analysis is a mel-frequency cepstral coefficient analysis.

3. The sound processing method according to claim 1 , wherein a model is generated by learning the sound signal based on the feature values extracted from the sound signal and identification information identifying the sound signal.

4. The sound processing method according to claim 3 , wherein the feature values are extracted from a newly detected sound signal, and the identification information corresponding to the feature values extracted from the newly detected sound signal is identified using the model.

5. The sound processing method according to claim 1 , wherein the feature values are extracted from a newly detected sound signal, and the sound signal is identified based on the feature values.

6. A sound processing apparatus comprising:

a memory storing processing instructions; and

at least one processor configured to execute the processing instructions, the processing instructions comprising:

performing a Fourier transform and then a cepstral analysis of a sound signal; and

extracting, as feature values of the sound signal, values including frequency components obtained by the Fourier transform of the sound signal, a zero-th-order component of a result obtained by the cepstral analysis of the sound signal, and a differential component of the result obtained by the cepstral analysis of the sound signal.

7. The sound processing apparatus according to claim 6 , wherein the cepstral-analysis is a mel-frequency cepstral coefficient analysis.

8. The sound processing apparatus according to claim 6 , wherein the processing instructions comprise generating a model by learning the sound signal based on the feature values extracted from the sound signal and identification information identifying the sound signal.

9. The sound processing apparatus according to claim 8 , wherein the processing instructions comprise extracting the feature values from a newly detected sound signal and identifying the identification information corresponding to the feature values extracted from the newly detected sound signal using the model.

10. The sound processing apparatus according to claim 6 , wherein the processing instructions comprise extracting the feature values from a newly detected sound signal and identifying the sound signal based on the feature values extracted from the newly detected sound signal.

11. A non-transitory computer-readable storage medium storing a program for causing an information processing apparatus to perform a process comprising:

performing a Fourier transform and then a cepstral analysis of a sound signal; and

extracting, as feature values of the sound signal, values including frequency components obtained by the Fourier transform of the sound signal, a zero-th-order component of a result obtained by the cepstral analysis of the sound signal, and a differential component of the result obtained by the cepstral analysis of the sound signal.

12. The non-transitory computer-readable storage medium storing the program according to claim 11 , wherein the program causes the information processing apparatus to perform a process of generating a model by learning the sound signal based on the feature values extracted from the sound signal and identification information identifying the sound signal.

13. The non-transitory computer-readable storage medium storing the program according to claim 12 , wherein

the program causes the information processing apparatus to perform a process of extracting the feature values from a newly detected sound signal and identifying the identification information corresponding to the feature values extracted from the newly detected sound signal using the model.

14. The non-transitory computer-readable storage medium storing the program according to claim 11 , wherein

the program causes the information processing apparatus to perform a process of extracting the feature values from the newly detected sound signal and identifying the sound signal based on the feature values extracted from a newly detected sound signal.

15. The sound processing method according to claim 1 , wherein the frequency components, the zero-th-order component, and the differential component are expressed as a set of numerical sequences in a time-series manner, and are used as the feature values.

16. The sound processing apparatus according to claim 6 , wherein the frequency components, the zero-th-order component, and the differential component are expressed as a set of numerical sequences in a time-series manner, and are used as the feature values.

17. The non-transitory computer-readable storage medium storing the program according to claim 11 , wherein the frequency components, the zero-th-order component, and the differential component are expressed as a set of numerical sequences in a time-series manner, and are used as the feature values.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 2, 2021
From: SENDODA, MITSURU
To: NEC CORPORATION
Reel/Frame 057368/0467 →
Priority Claims (1)
JP 2019-042431 · Mar 8, 2019 · national
Continuity (1)
Related Publication 20220051687A1 · Feb 17, 2022