Traditional birdsong recognition approaches used acoustic features based on the acoustic model of speech production or the perceptual model of the human auditory system to identify the associated bird species. In this paper, a new feature descriptor that uses image shape features is proposed to identify bird species based on the recognition of fixed-duration birdsong segments where their corresponding spectrograms are viewed as gray-level images. The MPEG-7 angular radial transform (ART) descriptor, which can compactly and efficiently describe the gray-level variations within an image region in both angular and radial directions, will be employed to extract the shape features from the spectrogram image. To effectively capture both frequency and temporal variations within a birdsong segment using ART, a sector expansion algorithm is proposed to transform its spectrogram image into a corresponding sector image such that the frequency and temporal axes of the spectrogram image will align with the radial and angular directions of the ART basis functions, respectively. For the classification of 28 bird species using Gaussian mixture models (GMM), the best classification accuracy is 86.30% and 94.62% for 3-second and 5-second birdsong segments using the proposed ART descriptor, which is better than traditional descriptors such as LPCC, MFCC, and TDMFCC.

In this paper, we will propose a 3D model retrieval approach using 2D cepstral features. First, six projection planes representing the elevation (depth) value are generated. Then, 2D cepstral features are extracted from each projection plane for searching similar 3D models. Experiments conducted on the Princeton Shape Benchmark (PSB) database have shown that the proposed 2D cepstral features outperforms other state-of-the-art descriptors in terms of the DCG score.

In this paper, we will propose an automatic music genre classification approach based on long-term modulation spectral analysis of spectral (OSC and MPEG-7 NASE) as well as cepstral (MFCC) features. Modulation spectral analysis of every feature value will generate a corresponding modulation spectrum and all the modulation spectra can be collected to form a modulation spectrogram which exhibits the time-varying or rhythmic information of music signals. Each modulation spectrum is then decomposed into several logarithmically-spaced modulation subbands. The modulation spectral contrast (MSC) and modulation spectral valley (MSV) are then computed from each modulation subband. Effective and compact features are generated from statistical aggregations of the MSCs and MSVs of all modulation subbands. An information fusion approach which integrates both feature level fusion method and decision level combination method is employed to improve the classification accuracy. Experiments conducted on two different music datasets have shown that our proposed approach can achieve higher classification accuracy than other approaches with the same experimental setup.

In this paper, an automatic music genre classification approach which integrates the features derived from static and transitional information of cepstral (MFCC) and spectral (OSC) features will be proposed. MFCC and OSC capture the characteristics of one audio frame. Therefore, the transitional information, including delta-MFCC, delta-OSC, delta-delta-MFCC, and delta-delta-OSC, are then extracted and combined with MFCC and OSC to improve the classification accuracy. Two information fusion techniques, including feature level fusion and decision level fusion, are developed to combine the extracted feature vectors. Experiments conducted on the music database employed in the ISMIR2004 Audio Description Contest have shown that the proposed approach can achieve a classification accuracy of 84.23%, which is better than the winner of the ISMIR2004 Music Genre Classification Contest.

In recent years, the demand for a content-based 3D model retrieval system becomes an important issue. In this study, a novel feature, derivative elevation descriptor (DED), is proposed to extract the exterior information of 3D model. The derivative elevation descriptor (DED) is combined with the elevation descriptor (ED) to retrieval 3D models based on the exterior shape information. Moreover, to derive better retrieval results, we will combine the exterior features with an interior feature 3D-ART by a novel relevance feedback approach. The experiments are conducted on the Princeton Shape Benchmark (PSB) database. Experiment results show that our proposed method is superior to others.

In this paper, we proposed a novel feature, called octave-based modulation spectral contrast (OMSC), for music genre classification. OMSC is extracted from long-term modulation spectrum analysis to represent the time-varying behavior of music signals. Experimental results have shown that OMSC outperforms MFCC and OSC. If OMSC is integrated with MFCC and OSC, the classification accuracy is 84.03% for seven music genre classification.

In this paper, a color image retrieval method based on the primitives of images will be proposed. First, the context of each pixel in an image will be defined. Then, the contexts in the image are clustered into several classes based on the algorithm of fast noniterative clustering. The mean of the context in the same class is considered as a primitive of the image. The primitives are used as feature vectors. Since the numbers of primitives between images are different, a specially designed similarity measure is then proposed to do color image retrieval. To better adapt to the preferences of users, a relevance feedback algorithm is provided to automatically determine the weight of each primitive according to the user's response. To demonstrate the effectiveness of the proposed system, several test databases from Corel are used to compare the performances of the proposed system with other methods. The experimental results show that the proposed system is superior to others.