Systems and methods for automatic determination of infant cry and discrimination of cry from fussiness
First Claim
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1. A system comprising:
- one or more processors; and
one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform;
receiving one or more datasets of audio data of a key child captured in a natural sound environment of the key child;
segmenting each of the one or more datasets of audio data to create audio segments, the audio segments comprising cry-related segments and non-cry segments;
determining periods of the cry-related segments that satisfy one or more threshold non-sparsity criteria; and
performing a classification on the periods to classify each of the periods as either a cry period or a fussiness period,wherein;
performing the classification on the periods comprises;
applying a binary logistic regression model on features of the periods; and
the features of the periods used in the binary logistic regression model comprise;
a slope of the period;
a median decibel sound pressure level of the period;
a total number of the periods of the cry-related segments, including (a) the periods of the cry-related segments that satisfy the one or more threshold non-sparsity criteria and (b) the periods of the cry-related segments that do not satisfy the one or more threshold non-sparsity criteria;
an age of the key child; and
a gender of the key child.
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Abstract
A method including receiving one or more datasets of audio data of a key child captured in a natural sound environment of the key child. The method also includes segmenting each of the one or more datasets of audio data to create audio segments. The audio segments include cry-related segments and non-cry segments. The method additionally includes determining periods of the cry-related segments that satisfy one or more threshold non-sparsity criteria. The method further includes performing a classification on the periods to classify each of the periods as either a cry period or a fussiness period. Other embodiments are described.
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Citations
22 Claims
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1. A system comprising:
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one or more processors; and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform; receiving one or more datasets of audio data of a key child captured in a natural sound environment of the key child; segmenting each of the one or more datasets of audio data to create audio segments, the audio segments comprising cry-related segments and non-cry segments; determining periods of the cry-related segments that satisfy one or more threshold non-sparsity criteria; and performing a classification on the periods to classify each of the periods as either a cry period or a fussiness period, wherein; performing the classification on the periods comprises; applying a binary logistic regression model on features of the periods; and
the features of the periods used in the binary logistic regression model comprise;a slope of the period; a median decibel sound pressure level of the period; a total number of the periods of the cry-related segments, including (a) the periods of the cry-related segments that satisfy the one or more threshold non-sparsity criteria and (b) the periods of the cry-related segments that do not satisfy the one or more threshold non-sparsity criteria; an age of the key child; and a gender of the key child. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method being implemented via execution of computing instructions configured to run on one or more processors and stored on one or more non-transitory computer-readable media, the method comprising:
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receiving one or more datasets of audio data of a key child captured in a natural sound environment of the key child; segmenting each of the one or more datasets of audio data to create audio segments, the audio segments comprising cry-related segments and non-cry segments; determining periods of the cry-related segments that satisfy one or more threshold non-sparsity criteria; and performing a classification on the periods to classify each of the periods as either a cry period or a fussiness period, wherein; performing the classification on the periods comprises; applying a binary logistic regression model on features of the periods; and
the features of the periods used in the binary logistic regression model comprise;a slope of the period; a median decibel sound pressure level of the period; a total number of the periods of the cry-related segments, including (a) the periods of the cry-related segments that satisfy the one or more threshold non-sparsity criteria and (b) the periods of the cry-related segments that do not satisfy the one or more threshold non-sparsity criteria; an age of the key child; and a gender of the key child. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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Specification