Method for identifying a momentary acoustic scene, application of said method, and a hearing device

1. A method for identifying a momentary acoustic scene, said method including

an extraction, during an extraction phase, of characteristics from an acoustic signal captured by at least one microphone ( 2 a , 2 b ), wherein at least auditory characteristics are extracted and

an identification, during an identification phase, of the momentary acoustic scene on the basis of the extracted characteristics by mapping the extracted characteristics to specific individual sound sources of a plurality of different sound sources and

selecting and executing a process for analyzing and modifying an acoustic signal, said process taken from a plurality of available processes based on the identified momentary acoustic scene.

2. Method as in claim 1 , wherein, for the identification of the characteristic features during the extraction phase, Auditory Scene Analysis (ASA) techniques are employed.

3. Method as in claim 1 , wherein, during the identification phase, Hidden Markov Model (HMM) techniques are employed for the identification of the momentary acoustic scene.

4. Method as in claim 1 , wherein at least one of the following auditory characteristics are identified during the extraction of said characteristic features: loudness, spectral pattern, harmonic structure, common build-up and decay processes, coherent amplitude modulations, coherent frequency modulations, coherent frequency transitions and binaural effects.

5. Method as in claim 1 , wherein at least one non-auditory characteristic is identified in addition to the auditory characteristics.

6. Method as claim 1 , wherein the auditory characteristics are grouped along Gestalt theory principles.

7. Method as in claim 6 , wherein the extraction of characteristics and/or the grouping of the characteristics are performed either in context-free or in context-sensitive fashion, and further including the step of taking into account information relative to a signal content to thereby provide an adaptation to the acoustic scene.

8. Method as in claim 1 , wherein, during the identification phase, data are accessed which were acquired in an off-line training phase.

9. A method for identifying and selecting an appropriate process for analyzing an acoustic signal, said method including

an extraction, during an extraction phase, of characteristics from said acoustic signal, wherein at least auditory characteristics are extracted ;

an identification, during an identification phase, of a momentary acoustic scene on the basis of the extracted characteristics by mapping the extracted characteristics to specific individual sound sources of a plurality of different sound sources;

selecting a process for analyzing the acoustic signal based on the identified momentary acoustic scene, wherein said suitable process is chosen from a plurality of available processes for analyzing the acoustic signal; and

executing said selected process to generate and output a processed acoustic signal.

10. The process of claim 9 , wherein said extraction includes the step of analyzing the acoustic structure of the acoustic signal for identifying tonal signals in acoustical signals generated by speech and tonal signals generated by music.

11. The process of claim 9 , wherein said extraction applies the principles of gestalt analysis for acoustical signals generated by speech and tonal signals generated by music.

12. The process of claim 11 , wherein said gestalt analysis includes examining a qualitative property chosen from the group consisting of continuity, proximity, similarity, common density, unit, and good constancy.

13. The process of claim 9 , wherein said executing said selected suitable process includes the step of processing said acoustic signal to generate a hearing signal for improving the hearing ability of a user.

14. The process of claim 9 , further including the step of generating an audio signal from said processed acoustic signal for transmission to a user.

15. A method for identifying and selecting an appropriate process for analyzing an acoustic signal, said method including

an extraction, during an extraction phase, of characteristics from said acoustic signal including the step of analyzing the acoustic structure of the acoustic signal for identifying tonal signals in acoustical signals generated by speech and tonal signals generated by music, wherein at least auditory characteristics are extracted ; and

an identification, during an identification phase, of a momentary acoustic scene on the basis of the extracted characteristics by mapping the extracted characteristics to each of a plurality of specific individual sound sources, and further wherein said identification includes the use of hidden markov models; and

selecting a process for analyzing the acoustic signal based on the identified momentary acoustic scene, wherein said suitable process is chosen from a plurality of available processes, said process for improving the hearing ability of a user;

executing said selected process, said executing including the step of processing said acoustic signal to generate a processed audio signal; and

generating an audio signal from said processed acoustic signal for transmission to said user.

16. A method for identifying and selecting an appropriate process for analyzing an acoustic signal, said method including:

an extraction of at least auditory-based characteristic features from an acoustic signal, wherein said auditory characteristics include one or more of: volume, spectral pattern, harmonic structure, common build-up and decay times, coherent amplitude modulations, coherent frequency modulations, coherent frequency transitions, and binaural effects; and

an identification of the momentary acoustic scene on the basis of the characteristics not limited to speech characteristics; and

automatically selecting a hearing process for execution by a hearing device from a plurality of available processes based on the identified momentary acoustic scene.

17. The method of claim 16 , wherein said identification includes at least a determination of whether the momentary acoustic scene includes speech, music, or some other auditory activity.

18. The method of claim 16 , further comprising a step of grouping the characteristic features according to: continuity, proximity, similarity, common density, unit, and good constancy; wherein said grouping supports the identification of the momentary acoustic scene.

19. A method for identifying a momentary acoustic scene for a hearing device, said method including

an extraction, during an extraction phase, of characteristics from an acoustic signal captured by at least one microphone, wherein at least auditory characteristics are extracted and

an identification, during an identification phase, of the momentary acoustic scene on the basis of the extracted characteristics; and

selecting and executing an audio signal analyzing process from a plurality of available audio signal analyzing processes based on the identified momentary acoustic scene, said audio signal analyzing process for execution in a hearing device for improving the hearing of a user.

20. The method of claim 19 , further comprising a step of grouping the characteristic features according to: continuity, proximity, similarity, common density, unit, and good constancy; wherein said grouping supports the identification of the momentary acoustic scene.

21. The process of claim 19 , wherein said execution generates a processed acoustic signal, said process further including the step of said hearing device generating an audio signal from said processed acoustic signal for transmission to a user to aid the hearing of the user.