Chang-Hong Lin

Mixed Sound Event Verification on Wireless Sensor Network for Home Automation

In this paper, we present the problem of mixed sound event verification in a wireless sensor network for home automation systems. In home automation systems, the sound recognized by the system becomes the basis for performing certain tasks. However, if a target source is mixed with another sound due to simultaneous occurrence, the system would generate poor recognition results, subsequently leading to inappropriate responses. To handle such problems, this study proposes a framework, which consists of sound separation and sound verification techniques based on a wireless sensor network (WSN), to realize sound-triggered automation. In the sound separation phase, we present a convolutive blind source separation system with source number estimation using time-frequency clustering. An accurate mixing matrix can be estimated by the proposed phase compensation technique and used for reconstructing the separated sound sources. In the verification phase, Mel frequency cepstral coefficients and Fisher scores that are derived from the wavelet packet decomposition of signals are used as features for support vector machines. Finally, a sound of interest can be selected for triggering automated services according to the verification result. The experimental results demonstrate the robustness and feasibility of the proposed system for mixed sound verification in WSN-based home environments.

Gabor-Based Nonuniform Scale-Frequency Map for Environmental Sound Classification in Home Automation

This work presents a novel feature extraction approach called nonuniform scale-frequency map for environmental sound classification in home automation. For each audio frame, important atoms from the Gabor dictionary are selected by using the Matching Pursuit algorithm. After the system disregards phase and position information, the scale and frequency of the atoms are extracted to construct a scale-frequency map. Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are then applied to the scale-frequency map, subsequently generating the proposed feature. During the classification phase, a segment-level multiclass Support Vector Machine (SVM) is operated. Experiments on a 17-class sound database indicate that the proposed approach can achieve an accuracy rate of 86.21%. Furthermore, a comparison reveals that the proposed approach is superior to the other time-frequency methods.

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.

Robust Environmental Sound Recognition With Fast Noise Suppression for Home Automation

This paper proposes a robust environmental sound recognition system using a fast noise suppression approach for home automation applications. The system comprises a fast subspace-based noise suppression module and a sound classification module. For the noise suppression module, we propose a noise suppression method that applies fast subspace approximations in the wavelet domain. We show that this method offers a lower computational cost than conventional methods. In the sound classification module, we use a feature extraction method that is also based on the wavelet subspace, derived from seventeen critical bands in a signals wavelet packet transform. Furthermore, we create a multiclass support vector machine by employing probability product kernels. The experimental results for ten classes of various environmental sounds show that the proposed system offers robust performance in environmental sound recognition tasks.

Speech emotion verification using emotion variance modeling and discriminant scale-frequency maps

This paper develops an approach to speech-based emotion verification based on emotion variance modeling and discriminant scale-frequency maps. The proposed system consists of two parts-feature extraction and emotion verification. In the first part, for each sound frame, important atoms from the Gabor dictionary are selected by using the matching pursuit algorithm. The scale, frequency, and magnitude of the atoms are extracted to construct a nonuniform scale-frequency map, which supports auditory discriminability by the analysis of critical bands. Next, sparse representation is used to transform scale-frequency maps into sparse coefficients to enhance the robustness against emotion variance and achieve error-tolerance improvement. In the second part, emotion verification, two scores are calculated. A novel sparse representation verification approach based on Gaussian-modeled residual errors is proposed to generate the first score from the sparse coefficients. Such a classifier can minimize emotion variance and improve recognition accuracy. The second score is calculated by using the emotional agreement index (EAI) from the same coefficients. These two scores are combined to obtain the final detection result. Experiments on an emotional database of spoken speech were conducted and indicate that the proposed approach can achieve an average equal error rate (EER) of as low as 6.61%. A comparison among different approaches reveals that the proposed method is superior to the others and confirms its feasibility.

Compressive Sensing-Based Speech Enhancement

This study proposes a speech enhancement method based on compressive sensing. The main procedures involved in the proposed method are performed in the frequency domain. First, an overcomplete dictionary is constructed from the trained speech frames. The atoms of this redundant dictionary are spectrum vectors that are trained by the K-SVD algorithm to ensure the sparsity of the dictionary. For a noisy speech spectrum, formant detection and a quasi-SNR criterion are first utilized to determine whether a frequency bin in the spectrogram is reliable, and a corresponding mask is designed. The mask-extracted reliable components in a speech spectrum are regarded as partial observations and a measurement matrix is constructed. The problem can therefore be treated as a compressive sensing problem. The K atoms of a K-sparsity speech spectrum are found using an orthogonal matching pursuit algorithm. Because the K atoms form the speech signal subspace, the removal of the noise projected onto these K atoms is achieved by multiplying the noisy spectrum with the optimized gain that corresponds to each selected atom. The proposed method is experimentally compared with the baseline methods and demonstrates its superiority.

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.

Spectral-temporal receptive fields and MFCC balanced feature extraction for noisy speech recognition

This paper aims to propose a new set of acoustic features based on spectral-temporal receptive fields (STRFs). The STRF is an analysis method for studying physiological model of the mammalian auditory system in spectral-temporal domain. It has two different parts: one is the rate (in Hz) which represents the temporal response and the other is the scale (in cycle/octave) which represents the spectral response. With the obtained STRF, we propose an effective acoustic feature. First, the energy of each scale is calculated from the STRF. The logarithmic operation is then imposed on the scale energies. Finally, the discrete Cosine transform is applied to generate the proposed STRF feature. In our experiments, we combine the proposed STRF feature with conventional Mel frequency cepstral coefficients (MFCCs) to verify its effectiveness. In a noise-free environment, the proposed feature can increase the recognition rate by 17.48%. Moreover, the increase in the recognition rate ranges from 5% to 12% in noisy environments.

Emotion Profile-Based Music Recommendation

In this paper, we propose an emotion profile based music recommendation system. In the proposed algorithm, two emotion profiles are constructed using decision value in support vector machine (SVM), and based on short term and long term features respectively. The recognized emotion, emotion profile, and personal historical query results are fed into the recommendation module to generate the recommended music list.

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.