Embodiments of training signal processing models for component replacement in signal processing systems are disclosed. A device for training a third signal processing model via a machine learning method include a first calculating unit, a second calculating unit and a training unit. The first calculating unit calculates a first output from each first sample in a first sample set based on a first signal processing model. The second calculating unit calculates a second output from the first sample based on a second signal processing model. The training unit trains a third signal processing model by minimizing a first cost including a first error between the first output and a third output, so that the combination of the second signal processing model and the third signal processing model can simulate the behaviors of the first signal processing model on the first sample set. The third output is an output of the third signal processing model in response to an input including the second output.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-42. (canceled) 43. A method of training a third signal processing model via a machine learning method, comprising steps of: a) calculating a first output from each sample in a first sample set based on a first signal processing model; b) calculating a second output from each sample in the first sample set based on a second signal processing model; and c) training a third signal processing model by minimizing a first cost including a first error between the first output and a third output, so that the combination of the second signal processing model and the third signal processing model can simulate the behavior of the first signal processing model on the first sample set, wherein the third output is an output of the third signal processing model in response to an input including the second output, wherein the first signal processing model and the second signal processing model are models for an audio content classifier and the first output and the second output are classification results; and wherein the third signal processing model is implemented as any one of a neural network, a mapping H with H=(QTQ)−1QTP, or a mapping H with H=(QTQ+r)−1QTP, where P and Q are M×N matrices, M is the number of samples in the first sample set, each row of P is one of the first outputs calculated from all the samples in the first sample set, each row of Q is one of the second outputs calculated from all the samples in the first sample set, and r is a regularization term for smoothing the mapping or avoiding over-fitting in the training process. 44. A method of training a third signal processing model via a machine learning method, comprising steps of: a) calculating a first output from each sample in a first sample set based on a first signal processing model; b) calculating a second output from each sample in the first sample set based on a second signal processing model; and c) training a third signal processing model by minimizing a first cost including a first error between the first output and a third output, so that the combination of the second signal processing model and the third signal processing model can simulate the behavior of the first signal processing model on the first sample set, wherein the third output is an output of the third signal processing model in response to an input including the second output, wherein the first signal processing model and the second signal processing model are models for an audio content classifier and the first output and the second output are classification results; wherein the step b) further comprises calculating a fourth output from each sample in a second sample set based on the second signal processing model, wherein the first signal processing model and the second signal processing model can have different behaviors on the second sample set; wherein the first cost includes the first error and a second error between the fourth output and a reference output; and wherein the third signal processing model is implemented as a mapping H: H=(αQXTQX+βQYTQY)−1(αQXTPX+βQYTLY), where α and β are weights, PX and QX are M×N matrices, M is the number of samples in the first sample set, each row of PX is one of the first outputs calculated from all the samples in the first sample set, and each row of QX is one of the second outputs calculated from all the samples in the first sample set, and LY and QY are K×N matrices, K is the number of samples in the second sample set, each row of LY is one of the reference outputs for all the samples in the second sample set, and each row of QY is one of the fourth outputs calculated from all the samples in the second sample set. 45. The method according to claim 43, wherein the input includes both the second output and the respective sample in the first sample set. 46. The method according to claim 43, comprising: d) training the second signal processing model by minimizing a second cost including an error between an output calculated from each of samples based on the first signal processing model and another output calculated from the same sample based on the second signal processing model. 47. The method according to claim 44, wherein the first cost is a weighted sum of the first error and the second error. 48. The method according to claim 43, wherein the third signal processing model is implemented as the neural network, and the neural network is a recurrent neural network. 49. A method of training a third signal processing model via a machine learning method, comprising steps of: a) acquiring a candidate set as a result of adding into an initial set one or more of sample sets which have not been added into the initial set; b) training a third signal processing model by calculating a second output from each sample in the candidate set based on a second signal processing model, and minimizing a first cost including an error calculated for each of the samples in the candidate set, so that the combination of the second signal processing model and the third signal processing model can simulate the behavior of a first signal processing model, wherein if the sample belongs to a sample set identified as a first type, the error is calculated as a difference between a first output calculated from the sample based on the first signal processing model, and a third output of the third signal processing model in response to an input including the second output, and if the sample belongs to a sample set identified as a second type, the error is calculated as a difference between a reference output and the third output; c) calculating an average of the errors calculated for the samples in the candidate set; and d) if the average is lower than an initial average associated with the initial set, setting the candidate set as the initial set, setting the average as the initial average and continuing to execute step a), and if the average is not lower than the initial average, continuing to execute step a), wherein the first signal processing model and the second signal processing model are models for an audio content classifier and the first output and the second output are classification results; and wherein the third signal processing model is implemented as a neural network. 50. The method according to claim 49, wherein the input includes both the second output and the sample. 51. The method according to claim 49, wherein the first signal processing model and the second signal processing model can have different behaviors on the sample set identified as the second type. 52. The method according to claim 49, comprising: training the second signal processing model by minimizing a second cost including an error between an output calculated from each of samples based on the first signal processing model and another output calculated from the same sample based on the second signal processing model. 53. The method according to claim 49, wherein the first cost is a weighted sum of the errors, where the errors calculated for the samples belonging to the same sample set are assigned with the same weight. 54. The method according to claim 53, wherein the errors calculated for the samples belonging to the sample sets identified as the same type are assigned with the same weight. 55. The method according to claim 49, wherein the third signal processing model is implemented as the neural network, and the neural network is a recurrent neural network.