59 results about "Audio signal classification" patented technology

A coding apparatus which suppresses an extreme increase in a bit rate, includes: a downmixing and coding unit (301) that downmixes audio signals that have been provided, to reduce the number of channels to be fewer than the number of the provided audio signals, and to code the downmix signals; an object parameter extracting unit (304) that extracts parameters indicating correlation between the audio signals; and a multiplexing circuit (309) that multiplexes the extracted parameters with the generated downmix coded signals. The object parameter extracting unit (304) includes: an object classifying unit (305) that classifies each of the provided audio signals into a predetermined one of types based on audio characteristics; and an object parameter extracting circuit (308) that extracts parameters using a temporal granularity and a frequency granularity each of which is determined for a corresponding one of the types.

The invention discloses a device and a method for audio classification and audio processing. In one implementation mode, the audio processing device comprises an audio classifier for classifying audio signals to at least one audio type in real time, an audio improving device for improving the experience of audiences and an adjusting unit for adjusting at least one parameter of the audio improving device based on a confidence value of at least one audio type in a continuous mode.

The invention discloses a method and system for detecting abnormal use of voice input equipment. A characteristic extracting mode which is more comprehensive to remote voice signal characteristics and closer to human perception is adopted in the invention so as to roughly judge background voice, normal voice and remote voice. On the basis of classification of audio signals, by adopting the combined method of a modern signal processing technology and a statistical machine learning theory, the problem of multiple limits on front-end voice input by a traditional method is overcome so that signal-level quality scoring is closer to human scoring. According to the invention, the difference problem of front-end input voice quality due to artificial use errors of the equipment in large-scale tests of spoken language is solved.

An audio signal quality enhancement apparatus and method. The apparatus includes a pitch calculating unit to extract a pitch period of an audio signal, a frequency domain transforming unit to transform the audio signal to a frequency domain, a frequency band dividing unit to classify the transformed audio signal into audio signals for each of the plurality of frequency bands based on the extracted pitch period, and a pitch enhancement unit to determine a gain based on a volume of the transformed audio signal, and to generate an output signal by multiplying each of the classified audio signals with respect to each of the plurality of frequency bands by the gain, thereby enhancing quality of the audio signal.

An audio signal classification method. The method comprises: according to the voice activity of a current audio frame, determining whether to obtain a frequency spectrum fluctuation of the current audio frame and store same in a frequency spectrum fluctuation memory (101); according to whether the audio frame is percussive music or the activity of a historical audio frame, updating the frequency spectrum fluctuation stored in the frequency spectrum fluctuation memory (102); and according to the statistics of some or all valid data of the frequency spectrum fluctuations stored in the frequency spectrum fluctuation memory, classifying the current audio frame as a voice frame or a music frame (103). Further provided is an audio signal classification device.

The invention discloses an audio signal sorting device in a broadband audio codec, wherein a background noise estimating and controlling module is used for receiving the spectral distribution parameter of a sorting parameter extracting module and sending the update rate to a signal initial sorting module; the signal initial sorting module is used for carrying out the initial sorting of the audio input signal according to the sub-band energy parameter and the update rate, and sending the initial sorting results to a sorted parameter extracting module and a signal sorting determining module; the sorted parameter extracting module is used for extracting and sorting the input signals, sending the sorting characteristic parameter of the acquired signal to the signal sorting determining module and feeding the acquired spectral distribution parameter back to the background noise estimating and controlling module at the same time; and the signal sorting determining module is used for setting the final sorting mark for the sorting characteristics parameter according the initial sorting results, wherein the final sorting mark is used for defining the determining sort of the output signal. The invention further discloses an audio signal sorting method in the broadband audio codec.

The invention relates to a support vector machine based classification method of base-band time-domain voice-frequency signals, comprising the following steps of: firstly segmenting a base-band time-domain voice-frequency signal sequence to obtain initial segmented subsequences; then respectively subtracting respective mean value from each initial segmented subsequence to obtain zero-mean-value segmented subsequences; then carrying out windowing treatment on each zero-mean-value segmented subsequence, respectively carrying out Fourier transformation treatment on results to obtain the spectrum amplitudes of the zero-mean-value segmented subsequences, and respectively solving the standard difference of each spectrum amplitude to obtain a characteristic quantity; sequentially combining the zero-mean-value segmented subsequences into a long subsequence according to an order; then calculating a normalized autocorrelation matrix of the long subsequence, and carrying out singular value decomposition on the normalized autocorrelation matrix to obtain a demarcation point of a subspace; then calculating the signal to noise ratio parameter of an other characteristic quantity; and finally sending an input vector composed of the two characteristic quantities into a trained SVM (Support Vector Machine) classifier to identify the classification of base-band time-domain voice-frequency signals and distinguish a voice signal and a noise signal.

An apparatus for classifying videos according to an embodiment of the present invention comprises: an audio extractor for extracting an audio signal upon receiving video information; an audio signal classifier for outputting primary classification information from the audio signal; and a video classifier for performing secondary classification on video data of the video information using the primary classification information.

In a system and method for noise estimation with music detection described herein provides for generating a music classification for music content in an audio signal. The music detector may classify the audio signal as music or non-music. The non-music signal may be considered to be signal and noise. An adaption rate may be adjusted responsive to the generated music classification. A noise estimate is calculated applying the adjusted adaption rate. The system and method may mitigate the noise modeling algorithms being misled by the music components.

The invention describes a method of classifying an audio input signal (1), said method comprising the steps of extracting a number of features (2) of the audio input signal (1), deriving a feature vector (3) for the input audio signal (1) based on these features (2), and determining the probability that the feature vector (3) for the input audio signal (1) falls within any of a number of classes (C1, C2, . . . , Cn), each corresponding to a particular release-date information.

The present invention discloses a method and a device for audio signal classification, and relates to the field of communications technologies, which solve a problem of high complexity of type classification of audio signals in the prior art. In the present invention, after an audio signal to be classified is received, a tonal characteristic parameter of the audio signal to be classified, where the tonal characteristic parameter of the audio signal to be classified is in at least one sub-band, is obtained, and a type of the audio signal to be classified is determined according to the obtained characteristic parameter. The present invention is mainly applied to an audio signal classification scenario, and implements audio signal classification through a relatively simple method.

The invention discloses an OSAHS diagnostic method based on a probability integrated regression model. The method comprises the steps that blood oxygen saturation is collected and subjected to preprocessing, and a snore signal is found according to the time corresponding to a descending segment of the blood oxygen saturation; feature extraction is conducted by using the Mel-frequency cepstrum coefficient, the features are input into a convolutional neural network, the audio signals of the descending section are classified into snore, breath and noise by combining a logistic regression model, and feature extraction is conducted on the processed snore and blood oxygen saturation; and finally, the probability integrated regression model is used for automatically predicting the sleep apnea hypopnea index of a patient to diagnose the OSAHS. Thus the patient can preliminarily detect the sleep state at home, the problem that the OSAHS patient queues in a hospital for PSG monitoring is solved,and meanwhile the pressure of doctors is relieved.