An autocorrelation calculation unit 21 calculates an autocorrelation R.sub.O(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R′.sub.O(i) obtained by multiplying a coefficient w.sub.O(i) by the autocorrelation R.sub.O(i). It is assumed here, for each order i of some orders i at least, that the coefficient w.sub.O(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.

An autocorrelation calculation unit 21 calculates an autocorrelation R.sub.O(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R′.sub.O(i) obtained by multiplying a coefficient w.sub.O(i) by the autocorrelation R.sub.O(i). It is assumed here, for each order i of some orders i at least, that the coefficient w.sub.O(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.

The present disclosure relates to systems and methods for temporally aligning media elements. Example methods include providing an audio input waveform based on an audio input and receiving a text input. The example method also includes converting the text input to a text-to-speech input waveform and extracting, with an audio feature extractor, characteristic audio features from the audio input waveform and the text-to-speech input waveform. The example method yet further includes comparing audio input waveform features and text-to-speech waveform features and, based on the comparison, temporally aligning a displayed version of the text input with the audio input.

The present disclosure relates to systems and methods for temporally aligning media elements. Example methods include providing an audio input waveform based on an audio input and receiving a text input. The example method also includes converting the text input to a text-to-speech input waveform and extracting, with an audio feature extractor, characteristic audio features from the audio input waveform and the text-to-speech input waveform. The example method yet further includes comparing audio input waveform features and text-to-speech waveform features and, based on the comparison, temporally aligning a displayed version of the text input with the audio input.

An audio processing method includes the following steps of capturing audio data by a microphone array to compute frequency array data of the audio data; computing a power sequence of degrees by using the frequency array data; and computing a difference value between a maximum value of the power sequence of degrees and a minimum value of the power sequence of degrees to determine whether the degree corresponding to the maximum value is a source degree relative to the microphone array.

An audio processing method includes the following steps of capturing audio data by a microphone array to compute frequency array data of the audio data; computing a power sequence of degrees by using the frequency array data; and computing a difference value between a maximum value of the power sequence of degrees and a minimum value of the power sequence of degrees to determine whether the degree corresponding to the maximum value is a source degree relative to the microphone array.

The present invention relates to transposing signals in time and/or frequency and in particular to coding of audio signals. More particular, the present invention relates to high frequency reconstruction (HFR) methods including a frequency domain harmonic transposer. A method and system for generating a transposed output signal from an input signal using a transposition factor T is described. The system comprises an analysis window of length L.sub.a, extracting a frame of the input signal, and an analysis transformation unit of order M transforming the samples into M complex coefficients. M is a function of the transposition factor T. The system further comprises a nonlinear processing unit altering the phase of the complex coefficients by using the transposition factor T, a synthesis transformation unit of order M transforming the altered coefficients into M altered samples, and a synthesis window of length L.sub.s, generating a frame of the output signal.

The present invention relates to transposing signals in time and/or frequency and in particular to coding of audio signals. More particular, the present invention relates to high frequency reconstruction (HFR) methods including a frequency domain harmonic transposer. A method and system for generating a transposed output signal from an input signal using a transposition factor T is described. The system comprises an analysis window of length L.sub.a, extracting a frame of the input signal, and an analysis transformation unit of order M transforming the samples into M complex coefficients. M is a function of the transposition factor T. The system further comprises a nonlinear processing unit altering the phase of the complex coefficients by using the transposition factor T, a synthesis transformation unit of order M transforming the altered coefficients into M altered samples, and a synthesis window of length L.sub.s, generating a frame of the output signal.

An autocorrelation calculation unit 21 calculates an autocorrelation R.sub.O(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R′.sub.O(i) obtained by multiplying a coefficient w.sub.O( ) by the autocorrelation R.sub.O(i). It is assumed here, for each order i of some orders i at least, that the coefficient w.sub.O(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.

An autocorrelation calculation unit 21 calculates an autocorrelation R.sub.O(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R′.sub.O(i) obtained by multiplying a coefficient w.sub.O( ) by the autocorrelation R.sub.O(i). It is assumed here, for each order i of some orders i at least, that the coefficient w.sub.O(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.