Image quality assessment using two-dimensional complex mel-cepstrum

Assessment of visual quality plays a crucial role in modeling, implementation, and optimization of image-and video-processing applications. The image quality assessment (IQA) techniques basically extract features from the images to generate objective scores. Feature-based IQA methods generally consist of two complementary phases: (1) feature extraction and (2) feature pooling. For feature extraction in the IQA framework, various algorithms have been used and recently, the two-dimensional (2-D) mel-cepstrum (2-DMC) feature extraction scheme has provided promising results in a feature-based IQA framework. However, the 2-DMC feature extraction scheme completely loses image-phase information that may contain high-frequency characteristics and important structural components of the image. In this work, "2-D complex mel-cepstrum" is proposed for feature extraction in an IQA framework. The method tries to integrate Fourier transform phase information into the 2-DMC, which was shown to be an efficient feature extraction scheme for assessment of image quality. Support vector regression is used for feature pooling that provides mapping between the proposed features and the subjective scores. Experimental results show that the proposed technique obtains promising results for the IQA problem by making use of the image-phase information. © 2016 SPIE and IS and T

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Cataloged from PDF version of article.Measurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (20) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics. (C) 2011 Elsevier Ltd. All rights reserved

COMPUTER BASED ENGLISH SPEAKING TEST BASED ON ARTIFICAL NEURAL NETWORK

English testing is a most common test conducted around the world for evaluating an individual’s English capabilities in mostly reading, writing, speaking, and listening domain. With increased cost and higher subjective assessment attached in some tests, there is required to change the test from traditional method to computer based. In this study, a proposed method for conducting speaking test for English based on objective assessment method. The proposed system is able to identify different dialects based on unit analysis of syllable along with phonetic errors. The proposed system is based on pronunciation parameters and neural network for evaluation purpose. The PSO algorithm is used for training the artificial neural network. The experiment result conducted for validating the proposed system shows promising performanc

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Measurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (2D) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics. © 2011 Elsevier Ltd. All rights reserved

IMPROVED LIP-READING LANGUAGE USING GATED RECURRENT UNITS

Lip-reading is one of the most challenging studies in computer vision. This is because lip-reading requires a large amount of training data, high computation time and power, and word length variation. Currently, the previous methods, such as Mel Frequency Cepstrum Coefficients (MFCC) with Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) with LSTM, still obtain low accuracy or long-time consumption because they use LSTM. In this study, we solve this problem using a novel approach with high accuracy and low time consumption. In particular, we propose to develop lip language reading by utilizing face detection, lip detection, filtering the amount of data to avoid overfitting due to data imbalance, image extraction based on CNN, voice extraction based on MFCC, and training model using LSTM and Gated Recurrent Units (GRU). Experiments on the Lip Reading Sentences dataset show that our proposed framework obtained higher accuracy when the input array dimension is deep and lower time consumption compared to the state-of-the-art

Percepcijska utemeljenost kepstranih mjera udaljenosti za primjene u obradi govora

Currently, one of the most widely used distance measures in speech and speaker recognition is the Euclidean distance between mel frequency cepstral coefficients (MFCC). MFCCs are based on filter bank algorithm whose filters are equally spaced on a perceptually motivated mel frequency scale. The value of mel cepstral vector, as well as the properties of the corresponding cepstral distance, are determined by several parameters used in mel cepstral analysis. The aim of this work is to examine compatibility of MFCC measure with human perception for different values of parameters in the analysis. By analysing mel filter bank parameters it is found that filter bank with 24 bands, 220 mels bandwidth and band overlap coefficient equal and higher than one gives optimal spectral distortion (SD) distance measures. For this kind of mel filter bank, the difference between vowels can be recognised for full-length mel cepstral SD RMS measure higher than 0.4 - 0.5 dB. Further on, we will show that usage of truncated mel cepstral vector (12 coefficients) is justified for speech recognition, but may be arguable for speaker recognition. We also analysed the impact of aliasing in cepstral domain on cepstral distortion measures. The results showed high correlation of SD distances calculated from aperiodic and periodic mel cepstrum, leading to the conclusion that the impact of aliasing is generally minor. There are rare exceptions where aliasing is present, and these were also analysed.Jedna od danas najčešće korištenih mjera u automatskom prepoznavanju govora i govornika je mjera euklidske udaljenosti MFCC vektora. Algoritam za izračunavanje mel frekvencijskih kepstralnih koeficijenata zasniva se na filtarskom slogu kod kojeg su pojasi ekvidistantno raspoređeni na percepcijski motiviranoj mel skali. Na vrijednost mel kepstralnog vektora, a samim time i na svojstva kepstralne mjere udaljenosti glasova, utječe veći broj parametara sustava za kepstralnu analizu. Tema ovog rada je ispitati usklađenost MFCC mjere sa stvarnim percepcijskim razlikama za različite vrijednosti parametara analize. Analizom parametara mel filtarskog sloga utvrdili smo da filtar sa 24 pojasa, širine 220 mel-a i faktorom preklapanja filtra većim ili jednakim jedan, daje optimalne SD mjere koje se najbolje slažu s percepcijom. Za takav mel filtarski slog granica čujnosti razlike između glasova je 0.4-0.5 dB, mjereno SD RMS razlikom potpunih mel kepstralnih vektora. Također, pokazat ćemo da je korištenje mel kepstralnog vektora odrezanog na konačnu dužinu (12 koeficijenata) opravdano za prepoznavanje govora, ali da bi moglo biti upitno u primjenama prepoznavanja govornika. Analizirali smo i utjecaj preklapanja spektara u kepstralnoj domeni na mjere udaljenosti glasova. Utvrđena je izrazita koreliranost SD razlika izračunatih iz aperiodskog i periodičkog mel kepstra iz čega zaključujemo da je utjecaj preklapanja spektara generalno zanemariv. Postoje rijetke iznimke kod kojih je utjecaj preklapanja spektara prisutan, te su one posebno analizirane

Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization

Pathological speech usually refers to speech distortion resulting from illness or other biological insults. The assessment of pathological speech plays an important role in assisting the experts, while automatic evaluation of speech intelligibility is difficult because it is usually nonstationary and mutational. In this paper, we carry out an independent innovation of feature extraction and reduction, and we describe a multigranularity combined feature scheme which is optimized by the hierarchical visual method. A novel method of generating feature set based on S-transform and chaotic analysis is proposed. There are BAFS (430, basic acoustics feature), local spectral characteristics MSCC (84, Mel S-transform cepstrum coefficients), and chaotic features (12). Finally, radar chart and F-score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96 dimensions based on NKI-CCRT corpus and 104 dimensions based on SVD corpus. The experimental results denote that new features by support vector machine (SVM) have the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus and 78.7% on SVD corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation

A survey on artificial intelligence-based acoustic source identification

The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions