100,735 research outputs found
Automatic Recognition of Arabic Poetry Meter from Speech Signal using Long Short-term Memory and Support Vector Machine
No full textThe recognition of the poetry meter in spoken lines is a natural language processing application that aims to identify a stressed and unstressed syllabic pattern in a line of a poem. Stateof-the-art studies include few works on the automatic recognition of Arud meters, all of which are text-based models, and none is voice based. Poetry meter recognition is not easy for an ordinary reader, it is very difficult for the listener and it is usually performed manually by experts. This paper proposes a model to detect the poetry meter from a single spoken line (“Bayt”) of an Arabic poem. Data of 230 samples collected from 10 poems of Arabic poetry, including three meters read by two speakers, are used in this work. The work adopts the extraction of linear prediction cepstrum coefficient and Mel frequency cepstral coefficient (MFCC) features, as a time series input to the proposed long short-term memory (LSTM) classifier, in addition to a global feature set that is computed using some statistics of the features across all of the frames to feed the support vector machine (SVM) classifier. The results show that the SVM model achieves the highest accuracy in the speakerdependent approach. It improves results by 3%, as compared to the state-of-the-art studies, whereas for the speaker-independent approach, the MFCC feature using LSTM exceeds the other proposed models
Maktabat Al Muthanna Baghdad Feb-May 1962
No full textOn the same date, Ali Al-Mansouri issued an official financial statement confirming that the Al-Khanji Foundation owed a total of 11.375.أصدر علي المنصوري بيانًا ماليًا رسميًا بتاريخ 25 نيسان 1962 يُفيد بأن مؤسسة الخانجي مدينة بمبلغ إجمالي قدره 11,375
Automatic Speech Emotion Recognition- Feature Space Dimensionality and Classification Challenges
No full textIn the last decade, research in Speech Emotion Recognition (SER) has become a major endeavour in Human Computer Interaction (HCI), and speech processing. Accurate SER is essential for many applications, like assessing customer satisfaction with quality of services, and detecting/assessing emotional state of children in care. The large number of studies published on SER reflects the demand for its use. The main concern of this thesis is the investigation of SER from a pattern recognition and machine learning points of view. In particular, we aim to identify appropriate mathematical models of SER and examine the process of designing automatic emotion recognition schemes. There are major challenges to automatic SER including ambiguity about the list/definition of emotions, the lack of agreement on a manageable set of uncorrelated speech-based emotion relevant features, and the difficulty of collected emotion-related datasets under natural circumstances. We shall initiate our work by dealing with the identification of appropriate sets of emotion related features/attributes extractible from speech signals as considered from psychological and computational points of views. We shall investigate the use of pattern-recognition approaches to remove redundancies and achieve compactification of digital representation of the extracted data with minimal loss of information. The thesis will include the design of new or complement existing SER schemes and conduct large sets of experiments to empirically test their performances on different databases, identify advantages, and shortcomings of using speech alone for emotion recognition. Existing SER studies seem to deal with the ambiguity/dis-agreement on a “limited” number of emotion-related features by expanding the list from the same speech signal source/sites and apply various feature selection procedures as a mean of reducing redundancies. Attempts are made to discover more relevant features to emotion from speech. One of our investigations focuses on proposing a newly sets of features for SER, extracted from Linear Predictive (LP)-residual speech. We shall demonstrate the usefulness of the proposed relatively small set of features by testing the performance of an SER scheme that is based on fusing our set of features with the existing set of thousands of features using common machine learning schemes of Support Vector Machine (SVM) and Artificial Neural Network (ANN). The challenge of growing dimensionality of SER feature space and its impact on increased model complexity is another major focus of our research project. By studying the pros and cons of the commonly used feature selection approaches, we argued in favour of meta-feature selection and developed various methods in this direction, not only to reduce dimension, but also to adapt and de-correlate emotional feature spaces for improved SER model recognition accuracy. We used rincipal Component Analysis (PCA) and proposed Data Independent PCA (DIPCA) by training on independent emotional and non-emotional datasets. The DIPCA projections, especially when extracted from speech data coloured with different emotions or from Neutral speech data, had comparable capability to the PCA in terms of SER performance. Another adopted approach in this thesis for dimension reduction is the Random Projection (RP) matrices, independent of training data. We have shown that some versions of RP with SVM classifier can offer an adaptation space for Speaker Independent SER that avoid over-fitting and hence improves recognition accuracy. Using PCA trained on a set of data, while testing on emotional data features, has significant implication for machine learning in general.
The thesis other major contribution focuses on the classification aspects of SER. We investigate the drawbacks of the well-known SVM classifier when applied to a preprocessed data by PCA and RP. We shall demonstrate the advantages of using the Linear Discriminant Classifier (LDC) instead especially for PCA de-correlated metafeatures.
We initiated a variety of LDC-based ensembles classification, to test performance of scheme using a new form of bagging different subsets of metafeature subsets extracted by PCA with encouraging results.
The experiments conducted were applied on two benchmark datasets (Emo-Berlin and FAU-Aibo), and an in-house dataset in the Kurdish language. Recognition accuracy achieved by are significantly higher than the state of art results on all datasets. The results, however, revealed a difficult challenge in the form of persisting wide gap in accuracy over different datasets, which cannot be explained entirely by the differences between the natures of the datasets. We conducted various pilot studies that were based on various visualizations of the confusion matrices for the “difficult” databases to build multi-level SER schemes. These studies provide initial evidences to the presence of more than one “emotion” in the same portion of speech.
A possible solution may be through presenting recognition accuracy in a score-based measurement like the spider chart. Such an approach may also reveal the presence of Doddington zoo phenomena in SER
An Energy Efficient Tour Construction Using Restricted k-Means Clustering Algorithm for Mobile Sink in Wireless Sensor Networks
No full textQilādat al-jawāhir fī dhikr al-Ghawth al-Rifāʻī wa-atbāʻih al-akābir
No full textA book on Sufism on the Rifa'i way, in which the author collects virtues, conditions, dignity, sayings, behavior, method, and the realizations of the truth of Sheikh Ahmed Muhyi al-Din Abu al-Abbas al-Kabeer al-Rifa'i. Furthermore, the user talked about the widespread support he receives from his followers and the key aspects of his method
Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network
No full textSign language assists in building communication and bridging gaps in understanding. Automatic sign language recognition (ASLR) is a field that has recently been studied for various sign languages. However, Kurdish sign language (KuSL) is relatively new and therefore researches and designed datasets on it are limited. This paper has proposed a model to translate KuSL into text and has designed a dataset using Kinect V2 sensor. The computation complexity of feature extraction and classification steps, which are serious problems for ASLR, has been investigated in this paper. The paper proposed a feature engineering approach on the skeleton position alone to provide a better representation of the features and avoid the use of all of the image information. In addition, the paper proposed model makes use of recurrent neural networks (RNNs)-based models. Training RNNs is inherently difficult, and consequently, motivates to investigate alternatives. Besides the trainable long short-term memory (LSTM), this study has proposed the untrained low complexity echo system network (ESN) classifier. The accuracy of both LSTM and ESN indicates they can outperform those in state-of-the-art studies. In addition, ESN which has not been proposed thus far for ASLT exhibits comparable accuracy to the LSTM with a significantly lower training time
Emotion recognition from speech: tools and challenges
No full textHuman emotion recognition from speech is studied frequently for its importance in many applications, e.g. human-computer interaction. There is a wide diversity and non-agreement about the basic emotion or emotion-related states on one hand and about where the emotion related information lies in the speech signal on the other side. These diversities motivate our investigations into extracting Meta-features using the PCA approach, or using a non-adaptive random projection RP, which significantly reduce the large dimensional speech feature vectors that may contain a wide range of emotion related information. Subsets of Meta-features are fused to increase the performance of the recognition model that adopts the score-based LDC classifier. We shall demonstrate that our scheme outperform the state of the art results when tested on non-prompted databases or acted databases (i.e. when subjects act specific emotions while uttering a sentence). However, the huge gap between accuracy rates achieved on the different types of datasets of speech raises questions about the way emotions modulate the speech. In particular we shall argue that emotion recognition from speech should not be dealt with as a classification problem. We shall demonstrate the presence of a spectrum of different emotions in the same speech portion especially in the non-prompted data sets, which tends to be more “natural” than the acted datasets where the subjects attempt to suppress all but one emotion. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only
Time Series-Based Spoof Speech Detection Using Long Short-Term Memory and Bidirectional Long Short-Term Memory
No full textDetecting fake speech in voice-based authentication systems is crucial for reliability. Traditional methods often struggle because they can't handle the complex patterns over time. Our study introduces an advanced approach using deep learning, specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models, tailored for identifying fake speech based on its temporal characteristics. We use speech signals with cepstral features like Mel-frequency cepstral coefficients (MFCC), Constant Q cepstral coefficients (CQCC), and open-source Speech and Music Interpretation by Large-space Extraction (OpenSMILE) to directly learn these patterns. Testing on the ASVspoof 2019 Logical Access dataset, we focus on metrics such as min-tDCF, Equal Error Rate (EER), Recall, Precision, and F1-score. Our results show that LSTM and BiLSTM models significantly enhance the reliability of spoof speech detection systems
Ash-Shuo'a" the UNDIVIDED by Imam Omar Bin Abdulaziz Al-Boukhary in the Hanafi School
No full textThis research treating a study and investigation of the book titled "Ash-Shuo'a" THE UNDIVIDED by Imam Omar Bin Abdulaziz Al-Boukhary in the Hanafi school. It contains an Introduction and two chapters, the introduction displays the reasons for choosing the research title, it's important, the previous studies, its objectives, its methodology and the abstract. The first chapter: the theoretical contains two themes. The first identify the author, the second identify the investigated book. The second chapter: the investigation which includes the methodology followed in the investigation of manuscript, the photos and the investigation of the book. Finally, I have showed the most important results and recommendations. Also, I mentioned the index of resources and references used in study and investigation
Musical Instruments in Al-Jahiz
No full textفي كتابات الجاحظ، تناول أديب العرب الجاحظ قضايا الموسيقى والطرب والغناء. كان الجاحظ معروفًا بعلمه وأدبه، وكتب العديد من الأعمال التي تسلط الضوء على جوانب الحياة المترفة في المجتمع. كتب كتابًا بعنوان "أخلاق المغنين" وآخر بعنوان "المغنين والغناء والصنعة". في هذه الكتب، دافع الجاحظ عن الغناء كفن فني، ورأى أنه يمتلك قواعد وأسسًا علمية تشابه مع غيره من الفنون والآداب. كان يروج للغناء باعتباره متعة فنية. وعلى الرغم من انتمائه للمعتزلة، إلا أن الجاحظ كان مشجعًا للغناء ومغنين، وكتب عن أخلاقهم وفنونهم. وفي رسالته "القيان"، تناول الجاحظ تأثير بيوت القيان والقيان نفسهن في المجتمع الإسلامي، حيث أشار إلى الفسق والعشق والفجور الذي قد ينتج عن هذا التأثير. بشكل عام، تركت كتابات الجاحظ أثرًا مهمًا في فهمنا لثقافة الموسيقى والطرب والغناء في العصور القديمة.In his writings, the Arab author Al-Jahiz addressed issues related to music, Tarab (a genre of music), and singing. Al-Jahiz was known for his knowledge and literature, and he wrote numerous works that shed light on the luxurious aspects of society. He authored a book titled "Ethics of Singers" and another titled "Singers, Singing, and the Craft." In these books, Al-Jahiz defended singing as an artistic form and believed that it possessed scientific principles and foundations similar to other arts and literature. He promoted singing as a pleasurable art form. Despite his affiliation with the Mu\u27tazila school of thought, Al-Jahiz was a supporter of singing and singers, and he wrote about their ethics and arts. In his treatise "Al-Qiyan," Al-Jahiz discussed the influence of courtesans and their households on Islamic society, pointing to the immorality, passion, and vice that may result from this influence. Overall, Al-Jahiz\u27s writings have had a significant impact on our understanding of music, Tarab, and singing in ancient times
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