Cepstrum

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

Environmental sound recognition using Gaussian mixture model and neural network classifier

Abstract: Environmental sound recognition is an audio scene identification process in which a person's location is found by analyzing the background sound. This paper deals with the prototype modeling for environmental sound recognition. Sound recognition involves the collection of audio data, extraction of important features, clustering of similar features and their classification. The Mel frequency cepstrum co-efficients are extracted. These features are used for clustering by a Gaussian mixture model which is a probabilistic model. Neural Network classifier is used for classification of the features and to identify the environmental audio scene. The implementation is done with the help of MATLAB. Five major environmental sounds which include the sound of car, office, restaurant, street, subway are considered. This shows a better efficiency than the already existing method. The efficiency achieved in this method is 98.9%.

Blurred image detection method fusing frequency spectrum information and cepstrum information

Abstract: The invention discloses a blurred image detection method fusing frequency spectrum information and cepstrum information, belongs to the technical field of image processing, and particularly relates to the detection technology of various blurred images. According to the blurred image detection method, first, an energy frequency spectrum distribution feature and a singularity cepstrum value histogram feature of an image are calculated, and serve as blur features of the image; second, a support vector machine classifier is selected for differentiating sharp image features from the blur image features, and collected images with demarcated blur categories is used for training corresponding parameters of the support vector machine classifier; finally, the trained support vector machine classifier is used for detecting whether an image to be detected is a blurred image. The blurred image detection method has the advantage that as a non-reference blurred image detection method, the blurred image detection method needs no reference image, thereby being wide in application range; meanwhile, the defined blur features have specific physical significance, and therefore the sharp image and the blurred image can be differentiated accurately.

Text independent speaker identification using fuzzy mathematical algorithm

Abstract: The difference in effectiveness in representing the individual features of speakers of line spectrum pair (LSP) frequencies and cepstrum derived from linear prediction analysis is demonstrated. A fuzzy mathematical algorithm is introduced. A fuzzy statistical method is used to build up the membership function as the speaker's template, and the maximum value of the membership function is used as the deciding criterion. A random combination of isolated digits from 0 to 9 is specified as the identification utterances. The system has been evaluated on a database of isolated digit utterances of 42 speakers, 20 males and 22 females, all university students. For each speaker, the training utterance time is approximately 36 seconds. The correct identification rate is approximately 99.7% for a random combination of five isolated digits. >

Speech recognition using regularized minimum variance distortionless response spectrum estimation-based cepstral features

Abstract: This paper presents regularized minimum variance distortion-less response (MVDR)-based cepstral features for robust continuous speech recognition. The mel-frequency cepstral coefficient (MFCC) features, widely used in speech recognition tasks, are usually computed from a direct spectrum estimate, that is, the squared magnitude of the discrete Fourier transform (DFT) of speech frames. Direct spectrum estimation methods (also known as nonparametric estimators) perform poorly under noisy and adverse conditions. To reduce this performance drop we propose to increase robustness of the speech recognition system by extracting more robust features based on the regularized MVDR technique. The proposed method, when evaluated on the AURORA-4 speech recognition task, provides an average relative improvement in word accuracy of 11.3%, 6.1%, and 5.2% over the conventional MFCC, PLP, MVDR and PMVDR-based MFCC features, respectively.

Using Nonlinear Features in Automatic English Lexical Stress Detection

Abstract: Lexical stress is an important prosodic feature, especially for stress-timed language such as English. This paper proposes three novel features, based on the nonlinear Bark scale and the Teager Energy Operator (TEO), for automatic English lexical stress detection. The proposed features are Bark Scale Cepstrum (BSC), Time Domain TEO-Bark Scale Cepstrum (TDT-BSC) and Frequency Domain TEO-Bark Scale Cepstrum (FDT-BSC). Their contributions, along with traditional features and their combinations, to English lexical stress detection are evaluated by single word pairs and continue sentences. Evaluation results showed that these new features gave significant improvement over traditional ones.