Acoustic data augmentation with mixed normalization factors
AssigneeInternational Business Machines Corporation
InventorsToru Nagano, Takashi Fukuda, Masayuki Suzuki
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Abstract
A method, computer system, and a computer program product for audio data augmentation are provided. Sets of audio data from different sources may be obtained. A respective normalization factor for at least two sources of the different sources may be calculated. The normalization factors from the at least two sources may be mixed to determine a mixed normalization factor. A first set of the sets may be normalized by using the mixed normalization factor and to obtain training data for training an acoustic model.
Claims20
A method for acoustic data augmentation, the method comprising:
- obtaining sets of audio data from different sources, wherein each of the respective sets comprises audio values with respect to time values over a respective time period;
- calculating a respective normalization factor for at least four sets of the sets of audio data, wherein the audio values of the at least four sets are for a same type of audio value;
- calculating a mixed normalization factor by using the at least four calculated normalization factors, wherein the mixed normalization factor is located at any point within a quadrilateral that includes the at least four calculated normalization factors;
- normalizing at least two sets of the at least four sets by using the mixed normalization factor, wherein the normalized at least two sets together constitute training data;
- and
- training an acoustic model by using the training data.
A computer system for acoustic data augmentation, the computer system comprising:
- one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more computer-readable tangible storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising:
- obtaining sets of audio data from different sources, wherein each of the respective sets comprises audio values with respect to time values over a respective time period;
- calculating a respective normalization factor for at least four sets of the sets of audio data, wherein the audio values of the at least four sets are for a same type of audio value;
- calculating a mixed normalization factor by using the at least four calculated normalization factors, wherein the mixed normalization factor is located at any point within a quadrilateral that includes the at least four calculated normalization factors;
- normalizing at least two sets of the at least four sets by using the mixed normalization factor, wherein the normalized at least two sets together constitute training data;
- and
- training an acoustic model by using the training data.
A computer program product for acoustic data augmentation, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the program instructions are executable by a computer system to cause the computer system to perform a method comprising:
- obtaining sets of audio data from different sources, wherein each of the respective sets comprises audio values with respect to time values over a respective time period;
- calculating a respective normalization factor for at least four sets of the sets of audio data, wherein the audio values of the at least four sets are for a same type of audio value;
- calculating a mixed normalization factor by using the at least four calculated normalization factors, wherein the mixed normalization factor is located at any point within a quadrilateral that includes the at least four calculated normalization factors;
- normalizing at least two sets of the at least four sets by using the mixed normalization factor, wherein the normalized at least two sets together constitute training data;
- and
- training an acoustic model by using the training data.
Dependencies are inferred from the claim text.