HARMONIC FEATURE PROCESSING FOR REDUCING NOISE

摘要

Devices, systems and methods are disclosed for reducing noise in input data by performing a hysteresis operation followed by a lateral excitation smoothing operation. For example, an audio signal may be represented as a sequence of feature vectors. A row of the sequence of feature vectors may, for example, be associated with the same harmonic of the audio signal at different points in time. To determine portions of the row that correspond to the harmonic being present, the system may compare an amplitude to a low threshold and a high threshold and select a series of data points that are above the low threshold and include at least one data point above the high threshold. The system may iteratively perform a spreading technique, spreading a center value of a center data point in a kernel to neighboring data points in the kernel, to further reduce noise.

主权项

1. A computer-implemented method for reducing noise in an audio signal, the method comprising:
obtaining a sequence of feature vectors from an audio signal, wherein each feature vector of the sequence is computed from a portion of the audio signal and represents the portion of the audio signal as a function of frequency; obtaining a low threshold and a high threshold; modifying the sequence of feature vectors by performing a hysteresis operation on a row of the sequence of feature vectors, wherein the row comprises element k of each feature vector of the sequence of feature vectors, and wherein the hysteresis operation comprises:
initializing all of the elements of the row to a first state,determining that a first element of the row is part of a continuous sequence of elements of the row, wherein (a) at least one element of the sequence is greater than the high threshold and (b) all elements of the sequence are greater than the low threshold,changing the state of the first element to a second state, andfor each element of the row having the first state, changing a value of the element; further modifying the sequence of feature vectors by:
obtaining a scale factor between 0 and 1,selecting element j of a first feature vector,determining a spread value by multiplying the scale factor by a value of the element j of the first feature vector, andadding the spread value to (i) element j+1 of the first feature vector, (ii) element j−1 of the first feature vector, (iii) element j of a feature vector subsequent to the first feature vector, and (iv) element j of a feature vector antecedent to the first feature vector; andgenerating output data including the sequence of feature vectors.