Cepstrum

About: Cepstrum is a research topic. Over the lifetime, 3346 publications have been published within this topic receiving 55742 citations.

On the use of acoustic-phonetic features in interactive labelling of multi-lingual speech corpora

Abstract: Results are reported from research on the use of continuously valued acoustic-phonetic features in the multi-language label alignment of combined speech corpora from three European languages: Danish, English, and Italian. A self-organizing neural network is used to transform cepstrum coefficients into a set of features, which are subsequently transformed into a set of principal components. These are used to model individual phonemes, which are used in a Viterbi search/level-building process to align an independently given string of phonemes with the corresponding speech signal. The results obtained show an overall accuracy of 55.7% in the positioning of the label boundary transitions in the combined test corpus. Detailed analysis shows that certain sound class boundaries are very accurately positioned. To provide a general solution to the problem of positioning badly positioned boundaries, an interactive component of the alignment system has been developed. First results demonstrate this component to be very valuable in the task of user-assisted boundary positioning. >

Bird voice recognition method using anti-noise power normalization cepstrum coefficients (APNCC)

Abstract: The invention provides a bird voice recognition technology based on novel noise-proof feature extraction by aiming at the problem of bird voice recognition in various kinds of background noise in ecological environment The bird voice recognition technology comprises the following steps of firstly, obtaining noise power spectrums by a noise estimation algorithm suitable for highly nonstationary environment; secondly, performing the noise reduction on the voice power spectrums by a multi-band spectral subtraction method; thirdly, extracting anti-noise power normalization cepstrum coefficients (APNCC) by combining the voice power spectrums for noise reduction; and finally, performing contrast experiments under the conditions of different environments and signal to noise ratios (SNR) on the voice of 34 species of birds by means of extracted APNCC, power normalization cepstrum coefficient (PNCC) and Mel frequency cepstrum coefficients (MFCC) by a support vector machine (SVM) The experiments show that the extracted APNCC have a better average recognition effect and higher noise robustness and are more suitable for bird voice recognition in the environment with less than 30 dB of SNR

Clustering of spatially relevant audio data using mel-frequency cepstrum for diagnosis of concrete structure by hammering test

Abstract: Hammering test is a popular non-destructive testing method which automation is highly demanded for efficient diagnosis of concrete structures. The objective is to correctly determine if a hammering sound originated from a defect in the structure or not. In this paper, we present an unsupervised learning approach to automation of hammering test for diagnosis of concrete structures among others. Sound samples are clustered using fuzzy clustering while incorporating physical spatial information and Mel-Frequency Cepstrum is used in order to reproduce human hearing when conducting hammering test. Experiments using concrete test blocks showed good results, both in single and multiple defects cases.

Determined BSS Based on Time-Frequency Masking and Its Application to Harmonic Vector Analysis

Abstract: This paper proposes harmonic vector analysis (HVA) based on a general algorithmic framework of audio blind source separation (BSS) that is also presented in this paper. BSS for a convolutive audio mixture is usually performed by multichannel linear filtering when the numbers of microphones and sources are equal (determined situation). This paper addresses such determined BSS based on batch processing. To estimate the demixing filters, effective modeling of the source signals is important. One successful example is independent vector analysis (IVA) that models the signals via co-occurrence among the frequency components in each source. To give more freedom to the source modeling, a general framework of determined BSS is presented in this paper. It is based on the plug-and-play scheme using a primal-dual splitting algorithm and enables us to model the source signals implicitly through a time-frequency mask. By using the proposed framework, determined BSS algorithms can be developed by designing masks that enhance the source signals. As an example of its application, we propose HVA by defining a time-frequency mask that enhances the harmonic structure of audio signals via sparsity of cepstrum. The experiments showed that HVA outperforms IVA and independent low-rank matrix analysis (ILRMA) for both speech and music signals. A MATLAB code is provided along with the paper for a reference.

Detection of the fundamental frequency in noisy environment for speech enhancement of a hearing aid

Abstract: This paper describes a method for noise-proof detection of the fundamental frequency of the voice in a noisy environment. Noise reduction techniques have been required in the development of a hearing aid, because noise makes intelligibility of hearing awfully inferior. In various methods of noise reduction, the fundamental frequency is often a significant parameter, but it is difficult to extract the frequency from the noisy voice. In order to utilize a comb filter method for noise reduction, a new method of detecting the fundamental frequency is developed by using the property of continuity in the fundamental frequency and the power spectrum envelope (PSE) of the human voice. The continuity of the PSE is utilized for determining the most reliable frequency. The gross pitch error (GPE) is reduced by the determination. Besides the frequency used for the comb filter is obtained from a linear predicting frequency and the latest fundamental frequency from the noisy voice, so as to suppress fluctuation of the frequency that degrades filtered voice. The procedure improves a fine pitch error (FPE) within 5%. The results of the evaluation showed that the present method proved to be superior to the traditional cepstrum method in the GPE and the FPE. We conclude that the proposed frequency detection method is available for noise reduction in the comb filter method.