Suwon Shon

Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification

Deep Neural Network (DNN) based transfer learning has been shown to be effective in Visual Object Classification (VOC) for complementing the deficit of target domain training samples by adapting classifiers that have been pre-trained for other large-scaled DataBase (DB). Although there exists an abundance of acoustic data, it can also be said that datasets of specific acoustic scenes are sparse for training Acoustic Scene Classification (ASC) models. By exploiting VOC DNNs ability of learning beyond its pre-trained environments, this paper proposes DNN based transfer learning for ASC. Effectiveness of the proposed method is demonstrated on the database of IEEE DCASE Challenge 2016 Task 1 and home surveillance environment via representative experiments. Its improved performance is verified by comparing it to prominent conventional methods.

Sudden noise source localization system for intelligent automobile application with acoustic sensors

This paper suggests an automotive application for finding direction of sudden noise source in driving situation. The system applies sound source localization algorithm using microphone array sensor and finds the direction of the abrupt abnormal noise sources. Representative experimental results demonstrate its feasibility as new safety car electronic component.

Deep neural network bottleneck features for acoustic event recognition

Bottleneck features have been shown to be effective in improving the accuracy of speaker recognition, language identification and automatic speech recognition. However, few works have focused on bottleneck features for acoustic event recognition. This paper proposes a novel acoustic event recognition framework using bottleneck features derived from a Deep Neural Network (DNN). In addition to conventional features (MFCC, Mel-spectrum, etc.), this paper employs rhythm, timbre, and spectrum-statistics features for effectively extracting acoustic characteristics from audio signals. The effectiveness of the proposed method is demonstrated on a database of real life recordings via experiments, and its robust performance is verified by comparing to conventional methods.

Maximum likelihood Linear Dimension Reduction of heteroscedastic feature for robust Speaker Recognition

This paper analyzes heteroscedasticity in i-vector for robust forensics and surveillance speaker recognition system. Linear Discriminant Analysis (LDA), a widely-used linear dimension reduction technique, assumes that classes are homoscedastic within a same covariance. In this paper it is assumed that general speech utterances contain both homoscedastic and heteroscedastic elements. We show the validity of this assumption by employing several analyses and also demonstrate that dimension reduction using principal components is feasible. To effectively handle the presence of heteroscedastic and homoscedastic elements, we propose a fusion approach of applying both LDA and Heteroscedastic-LDA (HLDA). The experiments are conducted to show its effectiveness and compare to other methods using the telephone database of National Institute of Standards and Technology (NIST) Speaker Recognition Evaluation (SRE) 2010 extended.

Abnormal acoustic event localization based on selective frequency bin in high noise environment for audio surveillance

In this paper, a method for source localization for surveillance system is presented. In particular, we propose an algorithm for abnormal acoustic event localization based on a novel approach of relevant frequency bin selections by statistical analyses. By means of selective frequency bin, it becomes possible to localize the event more accurately in high noise environment with low computational complexity. The effectiveness is verified through the experimental results in varied noise environments with different levels of Signal to Noise Ratio (SNR).

Robot user control system using hand gesture recognizer

This paper proposes a robot control human interface using Markov model (HMM) based hand signal recognizer. The command receiving humanoid robot sends webcam images to a client computer. The client computer then extracts the intended commanding hum n`s hand motion descriptors. Upon the feature acquisition, the hand signal recognizer carries out the recognition procedure. The recognition result is then sent back to the robot for responsive actions. The system performance is evaluated by measuring the recognition of `48 hand signal set` which is created randomly using fundamental hand motion set. For isolated motion recognition, `48 hand signal set` shows 97.07% recognition rate while the `baseline hand signal set` shows 92.4%. This result validates the proposed hand signal recognizer is indeed highly discernable. For the `48 hand signal set` connected motions, it shows 97.37% recognition rate. The relevant experiments demonstrate that the proposed system is promising for real world human-robot interface application.

Robust speaker direction estimation with microphone array using NMF for smart TV interaction

This paper proposes a robust speaker direction estimation method based on a microphone array for voice based interaction with smart TV. The proposed method uses speech basis and associated weights of non-negative matrix factorization for finding the speaker independent utterance direction from input signal with noise. The experimental results of the speaker direction estimation in real acoustic environment validate the effectiveness of the proposed algorithm in terms of representative performance measures compared to the conventional methods.

Non-negative matrix factorisation-based subband decomposition for acoustic source localisation

A novel non-negative matrix factorisation (NMF)-based subband decomposition in frequency–spatial domain for acoustic source localisation using a microphone array is introduced. The proposed method decomposes source and noise subband and emphasises source dominant frequency bins for more accurate source representation. By employing NMF, delay basis vectors and their subband information in frequency–spatial domain for each frame is extracted. The proposed algorithm is evaluated in both simulated noise and real noise with a speech corpus database. Experimental results clearly indicate that the algorithm performs more accurately than other conventional algorithms under both reverberant and noisy acoustic environments.

Generalized cross-correlation based noise robust abnormal acoustic event localization utilizing non-negative matrix factorization

In this paper, robust sound source localization for surveillance system is presented. In particular, we propose an algorithm for abnormal acoustic event localization using non-negative matrix factorization based frequency bin weighting. Based on the abnormal acoustic event localization experiments in real acoustic environment, the proposed algorithms excellent strength is validated in terms of representative performance measures compared to the conventional method.

Text Independent Speaker Verficiation Using Dominant State Information of HMM-UBM

We present a speaker verification method by extracting i-vectors based on dominant state information of Hidden Markov Model (HMM) - Universal Background Model (UBM). Ergodic HMM is used for estimating UBM so that various characteristic of individual speaker can be effectively classified. Unlike Gaussian Mixture Model(GMM)-UBM based speaker verification system, the proposed system obtains i-vectors corresponding to each HMM state. Among them, the i-vector for feature is selected by extracting it from the specific state containing dominant state information. Relevant experiments are conducted for validating the proposed system performance using the National Institute of Standards and Technology (NIST) 2008 Speaker Recognition Evaluation (SRE) database. As a result, 12 % improvement is attained in terms of equal error rate.