LEARNING DEVICE, LEARNING METHOD, AND PROGRAM
A learning device includes a learning unit that acquires learning data including data and a correct answer label assigned to the data and learns a weight of a DNN model for predicting an acoustic event from the learning data, the DNN model including: a feature extraction layer in which layers from an input layer to a predetermined intermediate layer have a structure similar to a structure of layers from an input layer to an intermediate layer of a CNN, and layers from the predetermined intermediate layer to an output layer have a structure similar to a structure of layers from an intermediate layer to an output layer of a SAN; and a prediction layer that predicts an event from an output of the feature extraction layer.
1 . A learning device comprising:
processing circuitry configured to
acquire learning data including data and a correct answer label assigned to the data and learn a weight of a DNN model for predicting an acoustic event from the learning data,
the DNN model including: a feature extraction layer in which layers from an input layer to a predetermined intermediate layer have a structure similar to a structure of layers from an input layer to an intermediate layer of a CNN, and layers from the predetermined intermediate layer to an output layer have a structure similar to a structure of layers from an intermediate layer to an output layer of a SAN; and a prediction layer that predicts an event from an output of the feature extraction layer.
2 . The learning device according to claim 1 , comprising:
processing circuitry configured to
extract a part of the learning data as a batch;
acquire the batch and calculate a risk in correct answer label learning for a purpose of multi-label classification by use of the DNN model and a Binary Cross Entropy loss function; and
acquire the risk and update the weight of the DNN model so as to minimize the risk.
3 . A learning method executed by a learning device, the learning method comprising
a learning step of acquiring learning data including data and a correct answer label assigned to the data and learning a weight of a DNN model for predicting an acoustic event from the learning data,
the DNN model including: a feature extraction layer in which layers from an input layer to a predetermined intermediate layer have a structure similar to a structure of layers from an input layer to an intermediate layer of a CNN, and layers from the predetermined intermediate layer to an output layer have a structure similar to a structure of layers from an intermediate layer to an output layer of a SAN; and a prediction layer that predicts an event from an output of the feature extraction layer.
4 . A program for causing a computer to function as the learning device according to claim 1 .
5 . A program for causing a computer to function as the learning device according to claim 2 .