Abstract: An estimation model construction method realized by a computer includes preparing a plurality of training data that include first training data that include first feature amount data that represent a first feature amount of a performance sound of a musical instrument and first onset data that represent a pitch at which an onset exists, and second training data that include second feature amount data that represent a second feature amount of sound generated by a sound source of a type different than the musical instrument, and second onset data that represent that an onset does not exist, and constructing, by machine learning using the plurality of training data, an estimation model that estimates, from a feature amount data that represent a feature amount of a performance sound of the musical instrument, estimated onset data that represent a pitch at which an onset exists.

Abstract: An audio signal analysis system comprises an electronic controller that is configured to execute a plurality of modules including an acquisition module configured to acquire a first spectrum, which is a time average of a plurality of frequency spectra of an audio signal, a specification module configured to acquire a plurality of reference values corresponding to different pitches that follow a prescribed temperament and configured to specify, by a problem-solving search algorithm, a frequency difference corresponding to a second spectrum which includes a plurality of components each having a frequency difference with respect to each of the plurality of reference values, the second spectrum being similar to the first spectrum with a degree of similarity exceeding a prescribed threshold value, and a correction module configured to correct the frequency difference so as to reduce systematic error included in the frequency difference specified by the specification module.

Abstract: An ear shape analysis method implemented by a computer includes generating a first ear shape data set by applying a first principal component weight vector to an ear shape model reflecting statistical tendencies of three-dimensional shapes of ears; and identifying from the generated first ear shape data set an estimated three-dimensional shape of a target ear corresponding to a target ear image represented by image data.

Abstract: An ear shape analyzer includes: a sample ear analyzer configured to generate, for each of N sample ears, an ear shape data set that represents a difference between a point group representative of a three-dimensional shape of a reference ear and a point group representative of a three-dimensional shape of one of the N sample ears; an averaging calculator configured to generate averaged shape data by averaging N ear shape data sets generated by the sample ear analyzer; an ear shape identifier configured to identify an average ear shape of the N sample ears by translating coordinates of respective points of the point group representing the three-dimensional shape of the reference ear, by using the averaged shape data.

Abstract: An ear shape analysis method implemented by a computer includes generating a first ear shape data set by applying a first principal component weight vector to an ear shape model reflecting statistical tendencies of three-dimensional shapes of ears; and identifying from the generated first ear shape data set an estimated three-dimensional shape of a target ear corresponding to a target ear image represented by image data.

Abstract: An ear shape analyzer includes: a sample ear analyzer configured to generate, for each of N sample ears, an ear shape data set that represents a difference between a point group representative of a three-dimensional shape of a reference ear and a point group representative of a three-dimensional shape of one of the N sample ears; an averaging calculator configured to generate averaged shape data by averaging N ear shape data sets generated by the sample ear analyzer; an ear shape identifier configured to identify an average ear shape of the N sample ears by translating coordinates of respective points of the point group representing the three-dimensional shape of the reference ear, by using the averaged shape data.