Yu-Hao Chin

Speaker Identification With Whispered Speech for the Access Control System

This work presents an access control system, which is a speaker identification system based on whispered speech. Speaker identification is a main function of an access control system. Hence, a novel speaker identification system using instantaneous frequencies is proposed. The input speech signals pass through both signal independent and signal dependent filters firstly. Then, we derive the signals instantaneous frequencies by applying the Hilbert transform. The analyzed instantaneous frequencies are proceeded to be modeled as probability density models. We use these probability density models as the feature in the proposed speaker identification system. In this work, we compare the use of parametric and nonparametric probability density estimation for instantaneous frequency modeling. Furthermore, we propose an approximated probability product kernel support vector machine (APPKSVM). In the APPKSVM, Riemann sum is applied in approximating the probability product kernel. The whisper sounds from the CHAIN speech corpus were used in the experiments. Results of the experiments show the superiority of the proposed speaker identification system.

Music emotion classification using double-layer support vector machines

This paper presents a two-layer system for detecting emotion in music. The selected target emotion classes are angry, happy, sad, and peaceful. We presented an audio feature set comprising the following types of audio features: dynamics, rhythm, timbre, pitch, and tonality. With the feature set, a support vector machines (SVMs) is applied to each target emotion class with calm emotion as the background class to train a hyperplane. With the four hyperplanes trained from angry, happy, sad, and peaceful, each test clip can output four decision values. This decision values are regarded as the new features to train a second-layer SVMs for classifying the four target emotion classes. The experiment result shows that our double layer system has a good performance on music emotion classification.

Hierarchical Dirichlet Process Mixture Model for Music Emotion Recognition

This study proposes a novel multi-label music emotion recognition (MER) system. An emotion cannot be defined clearly in the real world because the classes of emotions are usually considered overlapping. Accordingly, this study proposes an MER system that is based on hierarchical Dirichlet process mixture model (HPDMM), whose components can be shared between models of each emotion. Moreover, the HDPMM is improved by adding a discriminant factor to the proposed system based on the concept of linear discriminant analysis. The proposed system represents an emotion using weighting coefficients that are related to a global set of components. Moreover, three methods are proposed to compute the weighting coefficients of testing data, and the weighting coefficients are used to determine whether or not the testing data contain certain emotional content. In the tasks of music emotion annotation and retrieval, experimental results show that the proposed MER system outperforms state-of-the-art systems in terms of F-score and mean average precision.

Music Emotion Detection Using Hierarchical Sparse Kernel Machines

For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.

Predicting the Probability Density Function of Music Emotion using Emotion Space Mapping

Computationally modeling the affective content of music has been intensively studied in recent years because of its wide applications in music retrieval and recommendation. Although significant progress has been made, this task remains challenging due to the difficulty in properly characterizing the emotion of a music piece. Music emotion perceived by people is subjective by nature and thus complicates the process of collecting the emotion annotations as well as developing the predictive model. Instead of assuming people can reach a consensus on the emotion of music, in this work we propose a novel machine learning approach that characterizes the music emotion as a probability distribution in the valence-arousal (VA) emotion space, not only tackling the subjectivity but also precisely describing the emotions of a music piece. Specifically, we represent the emotion of a music piece as a probability density function (PDF) in the VA space via kernel density estimation from human annotations. To associate emotion with the audio features extracted from music pieces, we learn the combination coefficients by optimizing some objective functions of audio features, and then predict the emotion of an unseen piece by linearly combining the PDFs of the training pieces with the coefficients. Several algorithms for learning the coefficients are studied. Evaluations on the NTUMIR and MediaEval 2013 datasets validate the effectiveness of the proposed methods in predicting the probability distributions of emotion from audio features. We also demonstrate how to use the proposed approach in emotion-based music retrieval.

Automatic recognition of audio event using dynamic local binary patterns

This work proposes an automatic recognition system for recognizing audio events. First, an audio signal is converted into a spectrogram by short time Fourier transform. The acoustic background noises in the spectrogram are reduced by box filtering. The contrast of the spectrogram is then enhanced by VAR operation. With the enhanced spectrogram, this work further proposes a novel dynamic local binary pattern (DLBP) feature based on human auditory system. Finally, the DLBP features are fed to multi-class support vector machines to achieve the audio event recognition. The experimental results on 16 classes of audio events demonstrate the performance of the proposed audio event recognition system.

Genre based emotion annotation for music in noisy environment

The music listened by human is sometimes exposed to noise. For example, background noise usually exists when listening to music in broadcasts or lives. The noise will worsen the performance in various music emotion recognition systems. To solve the problem, this work constructs a robust system for music emotion classification in a noisy environment. Furthermore, the genre is considered when determining the emotional label for the song. The proposed system consists of three major parts, i.e. subspace based noise suppression, genre index computation, and support vector machine (SVM). Firstly, the system uses noise suppression to remove the noise content in the signal. After that, acoustical features are extracted from each music clip. Next, a dictionary is constructed by using songs that cover a wide range of genres, and it is adopted to implement sparse coding. Via sparse coding, data can be transformed to sparse coefficient vectors, and this paper computes genre indexes for the music genres based on the sparse coefficient vector. The genre indexes are regarded as combination weights in the latter phase. At the training stage of the SVM, this paper train emotional models for each genre. At the prediction stage, the predictions that obtained by emotional models in each genre are weighted combined across all genres using the genre indexes. Finally, the proposed system annotates multiple emotional labels for a song based on the combined prediction. The experimental result shows that the system can achieve a good performance in both normal and noisy environments.

Happiness detection in music using hierarchical SVMs with dual types of kernels

In this paper, we proposed a novel system for detecting happiness emotion in music. Two emotion profiles are constructed using decision value in support vector machine (SVM), and based on short term and long term feature respectively. When using short term feature to train models, the kernel used in SVM is probability product kernel. If the input feature is long term, the kernel used in SVM is RBF kernel. SVM model is trained from a raw feature set comprising the following types of features: rhythm, timbre, and tonality. Each SVM is applied to targeted emotion class with calm emotion as the background class to train hyperplanes respectively. With the eight hyperplanes trained from angry, happy, sad, relaxed, pleased, bored, nervous, and peaceful, each test clip can output four decision values, which are then regarded as the emotion profile. Two profiles are fusioned to train SVMs. The final decision value is then extracted to draw DET curve. The experiment result shows that the proposed system has a good performance on music emotion recognition.

Graph based orange computing architecture for cyber-physical being

In this paper a graph based orange computing architecture for cyber-physical Being (CPB) is proposed. The Physical world is not entirely predictable, thus Cyber Physical Systems (CPS) revitalize traditional computing with a contemporary real-world approach that can touch our day-today life. A graph is modeled to tackle the architectural optimization in a CPBs network. These frameworks of assisted method improve the quality of life for functionally locked in individuals. A learning pattern is developed to facilitate well-being of the individual. Surveys were performed for multiple CPBs for different state, activities and quality of life scheme are considered.

Kernel weighted Fisher sparse analysis on multiple maps for audio event recognition

This work presents a novel approach for audio event recognition. The approach develops a weighted kernel fisher sparse analysis method based on multiple maps. The proposed method consists of maps extraction and kernel weighted Fisher sparse analysis. Two maps are firstly extracted from each audio file, i.e. scale-frequency map and damping-frequency map. The scale and frequency of the Gabor atoms are extracted to construct a scale-frequency map. On the other hand, the damping-frequency map is generated according to the frequency and damping factor of damped atoms. Gabor atoms can be utilized to model human auditory perception, and the damped atoms can be used to model commonly observed damped oscillations in natural signals. This work fuses the advantages of these two dictionaries to improve the performance of the system. During the recognition stage, this work constructs a kernel sparse representation-based classifier via the proposed kernel weighted Fisher sparse analysis to enhance separability. The proposed kernel weighted Fisher sparse analysis combines sparse representation with heteroscedastic kernel weighted discriminant analysis (HKWDA), which is useful for providing a discriminative recognition of audio events because a weighted pairwise Chernoff criterion is utilized in the kernel space. Experiments on a 20-class audio event database indicate that the proposed approach can achieve an accuracy rate of 82.70%. Also, integrating the scale-frequency map with MFCCs increases the accuracy rate to 87.70%.