Ukrainian Catholic University
Faculty of Applied Sciences
Data Science Master Program
January 23d
Abstract. In modern days synthesis of human images and videos is arguably one of the most popular topics in the Data Science community. The synthesis of human speech is less trendy but deeply bonded to the mentioned topic. Since the publication of WaveNet paper by Google researchers in 2016, the state-of-the-art approach transferred from parametric and concatenative systems to deep learning models. Most of the work on the area focuses on improving the intelligibility and naturalness of the speech. However, almost every significant study also mentions ways to generate speech with the voices of different speakers. Usually, such an enhancement requires the model’s re-training in case of generating audio with the voice of a speaker that was not present in the training set. Additionally, studies focused on highly modular speech generation are rare. Therefore there is a room left for research on ways to add new parameters for other aspects of the speech, like sentiment, prosody, and melody. In this work, we aimed to implement a competitive text-to-speech solution with the ability to specify the speaker without model re-training and explore possibilities for adding emotions to the generated speech. Our approach generates good quality speech with the mean opinion score of 3,78 (out of 5) points and the ability to mimic speaker voice in real-time, which is a big improvement over the baseline that merely obtains 2,08. On top of that, we researched sentiment representation possibilities. We built an emotion classifier that performs on the level of the current state of the art solutions by giving an accuracy of more than eighty percent.
Speech Emotion Recognition by Using Combinations of Support Vector Machine (S...mathsjournal
Speech emotion recognition enables a computer system to records sounds and realizes the emotion of the
speaker. we are still far from having a natural interaction between the human and machine because
machines cannot distinguishes the emotion of the speaker. For this reason it has been established a new
investigation field, namely “the speech emotion recognition systems”. The accuracy of these systems
depend on the various factors such as the type and the number of the emotion states and also the classifier
type. In this paper, the classification methods of C5.0, Support Vector Machine (SVM), and the
combination of C5.0 and SVM (SVM-C5.0) are verified, and their efficiencies in speech emotion
recognition are compared. The utilized features in this research include energy, Zero Crossing Rate (ZCR),
pitch, and Mel-scale Frequency Cepstral Coefficients (MFCC). The results of paper demonstrate that the
effectiveness proposed SVM-C5.0 classification method is more efficient in recognizing the emotion of the
between -5.5 % and 8.9 % depending on the number of emotion states than SVM, C5.0.
Ukrainian Catholic University
Faculty of Applied Sciences
Data Science Master Program
January 23d
Abstract. In modern days synthesis of human images and videos is arguably one of the most popular topics in the Data Science community. The synthesis of human speech is less trendy but deeply bonded to the mentioned topic. Since the publication of WaveNet paper by Google researchers in 2016, the state-of-the-art approach transferred from parametric and concatenative systems to deep learning models. Most of the work on the area focuses on improving the intelligibility and naturalness of the speech. However, almost every significant study also mentions ways to generate speech with the voices of different speakers. Usually, such an enhancement requires the model’s re-training in case of generating audio with the voice of a speaker that was not present in the training set. Additionally, studies focused on highly modular speech generation are rare. Therefore there is a room left for research on ways to add new parameters for other aspects of the speech, like sentiment, prosody, and melody. In this work, we aimed to implement a competitive text-to-speech solution with the ability to specify the speaker without model re-training and explore possibilities for adding emotions to the generated speech. Our approach generates good quality speech with the mean opinion score of 3,78 (out of 5) points and the ability to mimic speaker voice in real-time, which is a big improvement over the baseline that merely obtains 2,08. On top of that, we researched sentiment representation possibilities. We built an emotion classifier that performs on the level of the current state of the art solutions by giving an accuracy of more than eighty percent.
Speech Emotion Recognition by Using Combinations of Support Vector Machine (S...mathsjournal
Speech emotion recognition enables a computer system to records sounds and realizes the emotion of the
speaker. we are still far from having a natural interaction between the human and machine because
machines cannot distinguishes the emotion of the speaker. For this reason it has been established a new
investigation field, namely “the speech emotion recognition systems”. The accuracy of these systems
depend on the various factors such as the type and the number of the emotion states and also the classifier
type. In this paper, the classification methods of C5.0, Support Vector Machine (SVM), and the
combination of C5.0 and SVM (SVM-C5.0) are verified, and their efficiencies in speech emotion
recognition are compared. The utilized features in this research include energy, Zero Crossing Rate (ZCR),
pitch, and Mel-scale Frequency Cepstral Coefficients (MFCC). The results of paper demonstrate that the
effectiveness proposed SVM-C5.0 classification method is more efficient in recognizing the emotion of the
between -5.5 % and 8.9 % depending on the number of emotion states than SVM, C5.0.
A critical insight into multi-languages speech emotion databasesjournalBEEI
With increased interest of human-computer/human-human interactions, systems deducing and identifying emotional aspects of a speech signal has emerged as a hot research topic. Recent researches are directed towards the development of automated and intelligent analysis of human utterances. Although numerous researches have been put into place for designing systems, algorithms, classifiers in the related field; however the things are far from standardization yet. There still exists considerable amount of uncertainty with regard to aspects such as determining influencing features, better performing algorithms, number of emotion classification etc. Among the influencing factors, the uniqueness between speech databases such as data collection method is accepted to be significant among the research community. Speech emotion database is essentially a repository of varied human speech samples collected and sampled using a specified method. This paper reviews 34 `speech emotion databases for their characteristics and specifications. Furthermore critical insight into the imitational aspects for the same have also been highlighted.
Deep Learning techniques have enabled exciting novel applications. Recent advances hold lot of promise for speech based applications that include synthesis and recognition. This slideset is a brief overview that presents a few architectures that are the state of the art in contemporary speech research. These slides are brief because most concepts/details were covered using the blackboard in a classroom setting. These slides are meant to supplement the lecture.
ASERS-CNN: ARABIC SPEECH EMOTION RECOGNITION SYSTEM BASED ON CNN MODELsipij
When two people are on the phone, although they cannot observe the other person's facial expression and physiological state, it is possible to estimate the speaker's emotional state by voice roughly. In medical care, if the emotional state of a patient, especially a patient with an expression disorder, can be known, different care measures can be made according to the patient's mood to increase the amount of care. The system that capable for recognize the emotional states of human being from his speech is known as Speech emotion recognition system (SER). Deep learning is one of most technique that has been widely used in emotion recognition studies, in this paper we implement CNN model for Arabic speech emotion recognition. We propose ASERS-CNN model for Arabic Speech Emotion Recognition based on CNN model. We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we compare the accuracy between our previous ASERS-LSTM and new ASERS-CNN model proposed in this paper and we comes out that our new proposed mode is outperformed ASERS-LSTM model where it get 98.18% accuracy
ASERS-LSTM: Arabic Speech Emotion Recognition System Based on LSTM Modelsipij
The swift progress in the study field of human-computer interaction (HCI) causes to increase in the interest in systems for Speech emotion recognition (SER). The speech Emotion Recognition System is the system that can identify the emotional states of human beings from their voice. There are well works in Speech Emotion Recognition for different language but few researches have implemented for Arabic SER systems and that because of the shortage of available Arabic speech emotion databases. The most commonly considered languages for SER is English and other European and Asian languages. Several machine learning-based classifiers that have been used by researchers to distinguish emotional classes: SVMs, RFs, and the KNN algorithm, hidden Markov models (HMMs), MLPs and deep learning. In this paper we propose ASERS-LSTM model for Arabic Speech Emotion Recognition based on LSTM model. We extracted five features from the speech: Mel-Frequency Cepstral Coefficients (MFCC) features, chromagram, Melscaled spectrogram, spectral contrast and tonal centroid features (tonnetz). We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we also construct a DNN for classify the Emotion and compare the accuracy between LSTM and DNN model. For DNN the accuracy is 93.34% and for LSTM is 96.81%
This power-point presentation contains 45 slides. It describes SR system (a brief intro), what are the applications, the biological architecture of human speech recognition vs machine architecture, recognition process, flow summery of recognition process and the approaches to the SRS. All this is described in the first few slides (the first part, let's say), after that, this presentation describes the evolution process of SRS through the decades (the middle part), and at the last this presentation describes the machine learning approach in SRS. How neural net enhance the efficiency of a SRS.
The project was started with a sole aim in mind that the design should be able to recognize the voice of a person by analyzing the speech signal. The simulation is done in MATLAB. The design of the project is based on using the Linear prediction filter coefficient (LPC) and Principal component analysis (PCA) on data (princomp) for the speech signal analysis. The Sample Collection process is accomplished by using the microphone to record the speech of male/female. After executing the program the speech is analyzed by the analysis part of our MATLAB program code and our design should be able to identify and give the judgment that the recorded speech signal is same as that of our desired output.
The following resources come from the 2009/10 B.Sc in Media Technology and Digital Broadcast (course number 2ELE0073) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
This presentation was delivered to a "Web Enabled Business" class at Simon Fraser University in Vancouver. The topic is speech recognition technology, and the presentation covers its origins, how it works, issues, latest trends and future opportunities.
Also known as automatic speech recognition or computer speech recognition which means understanding voice by the computer and performing any required task.
A critical insight into multi-languages speech emotion databasesjournalBEEI
With increased interest of human-computer/human-human interactions, systems deducing and identifying emotional aspects of a speech signal has emerged as a hot research topic. Recent researches are directed towards the development of automated and intelligent analysis of human utterances. Although numerous researches have been put into place for designing systems, algorithms, classifiers in the related field; however the things are far from standardization yet. There still exists considerable amount of uncertainty with regard to aspects such as determining influencing features, better performing algorithms, number of emotion classification etc. Among the influencing factors, the uniqueness between speech databases such as data collection method is accepted to be significant among the research community. Speech emotion database is essentially a repository of varied human speech samples collected and sampled using a specified method. This paper reviews 34 `speech emotion databases for their characteristics and specifications. Furthermore critical insight into the imitational aspects for the same have also been highlighted.
Deep Learning techniques have enabled exciting novel applications. Recent advances hold lot of promise for speech based applications that include synthesis and recognition. This slideset is a brief overview that presents a few architectures that are the state of the art in contemporary speech research. These slides are brief because most concepts/details were covered using the blackboard in a classroom setting. These slides are meant to supplement the lecture.
ASERS-CNN: ARABIC SPEECH EMOTION RECOGNITION SYSTEM BASED ON CNN MODELsipij
When two people are on the phone, although they cannot observe the other person's facial expression and physiological state, it is possible to estimate the speaker's emotional state by voice roughly. In medical care, if the emotional state of a patient, especially a patient with an expression disorder, can be known, different care measures can be made according to the patient's mood to increase the amount of care. The system that capable for recognize the emotional states of human being from his speech is known as Speech emotion recognition system (SER). Deep learning is one of most technique that has been widely used in emotion recognition studies, in this paper we implement CNN model for Arabic speech emotion recognition. We propose ASERS-CNN model for Arabic Speech Emotion Recognition based on CNN model. We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we compare the accuracy between our previous ASERS-LSTM and new ASERS-CNN model proposed in this paper and we comes out that our new proposed mode is outperformed ASERS-LSTM model where it get 98.18% accuracy
ASERS-LSTM: Arabic Speech Emotion Recognition System Based on LSTM Modelsipij
The swift progress in the study field of human-computer interaction (HCI) causes to increase in the interest in systems for Speech emotion recognition (SER). The speech Emotion Recognition System is the system that can identify the emotional states of human beings from their voice. There are well works in Speech Emotion Recognition for different language but few researches have implemented for Arabic SER systems and that because of the shortage of available Arabic speech emotion databases. The most commonly considered languages for SER is English and other European and Asian languages. Several machine learning-based classifiers that have been used by researchers to distinguish emotional classes: SVMs, RFs, and the KNN algorithm, hidden Markov models (HMMs), MLPs and deep learning. In this paper we propose ASERS-LSTM model for Arabic Speech Emotion Recognition based on LSTM model. We extracted five features from the speech: Mel-Frequency Cepstral Coefficients (MFCC) features, chromagram, Melscaled spectrogram, spectral contrast and tonal centroid features (tonnetz). We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we also construct a DNN for classify the Emotion and compare the accuracy between LSTM and DNN model. For DNN the accuracy is 93.34% and for LSTM is 96.81%
This power-point presentation contains 45 slides. It describes SR system (a brief intro), what are the applications, the biological architecture of human speech recognition vs machine architecture, recognition process, flow summery of recognition process and the approaches to the SRS. All this is described in the first few slides (the first part, let's say), after that, this presentation describes the evolution process of SRS through the decades (the middle part), and at the last this presentation describes the machine learning approach in SRS. How neural net enhance the efficiency of a SRS.
The project was started with a sole aim in mind that the design should be able to recognize the voice of a person by analyzing the speech signal. The simulation is done in MATLAB. The design of the project is based on using the Linear prediction filter coefficient (LPC) and Principal component analysis (PCA) on data (princomp) for the speech signal analysis. The Sample Collection process is accomplished by using the microphone to record the speech of male/female. After executing the program the speech is analyzed by the analysis part of our MATLAB program code and our design should be able to identify and give the judgment that the recorded speech signal is same as that of our desired output.
The following resources come from the 2009/10 B.Sc in Media Technology and Digital Broadcast (course number 2ELE0073) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
This presentation was delivered to a "Web Enabled Business" class at Simon Fraser University in Vancouver. The topic is speech recognition technology, and the presentation covers its origins, how it works, issues, latest trends and future opportunities.
Also known as automatic speech recognition or computer speech recognition which means understanding voice by the computer and performing any required task.
Audio Features Based Steganography Detection in WAV Fileijtsrd
Audio signals containing secret information or not is a security issue addressed in the context of steganalysis. ThRainfalle conceptual ide lies in the difference of the distribution of various statistical distance measures between the cover audio signals and stego audio signals. The aim of the propose system is to analyze the audio signal which have the presence of information hiding behavior or not. Mel frequency ceptral coefficient, zero crossing rate, spectral flux and short time energy features of audio signal are extracted, and combine these features with the features extracted from the modified version that is generated by randomly modifying with significant bits. Moreover, the extracted features are detected or classified with a support vector machine in this propose system. Experimental result show that the propose method performs well in steganalysis of the audio stegnograms that are produced by using S tools4. Khin Myo Kyi "Audio Features Based Steganography Detection in WAV File" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26807.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/26807/audio-features-based-steganography-detection-in-wav-file/khin-myo-kyi
Speech emotion recognition with light gradient boosting decision trees machineIJECEIAES
Speech emotion recognition aims to identify the emotion expressed in the speech by analyzing the audio signals. In this work, data augmentation is first performed on the audio samples to increase the number of samples for better model learning. The audio samples are comprehensively encoded as the frequency and temporal domain features. In the classification, a light gradient boosting machine is leveraged. The hyperparameter tuning of the light gradient boosting machine is performed to determine the optimal hyperparameter settings. As the speech emotion recognition datasets are imbalanced, the class weights are regulated to be inversely proportional to the sample distribution where minority classes are assigned higher class weights. The experimental results demonstrate that the proposed method outshines the state-of-the-art methods with 84.91% accuracy on the Berlin database of emotional speech (emo-DB) dataset, 67.72% on the Ryerson audio-visual database of emotional speech and song (RAVDESS) dataset, and 62.94% on the interactive emotional dyadic motion capture (IEMOCAP) dataset.
A Framework for Human Action Detection via Extraction of Multimodal FeaturesCSCJournals
This work discusses the application of an Artificial Intelligence technique called data extraction and a process-based ontology in constructing experimental qualitative models for video retrieval and detection. We present a framework architecture that uses multimodality features as the knowledge representation scheme to model the behaviors of a number of human actions in the video scenes. The main focus of this paper placed on the design of two main components (model classifier and inference engine) for a tool abbreviated as VASD (Video Action Scene Detector) for retrieving and detecting human actions from video scenes. The discussion starts by presenting the workflow of the retrieving and detection process and the automated model classifier construction logic. We then move on to demonstrate how the constructed classifiers can be used with multimodality features for detecting human actions. Finally, behavioral explanation manifestation is discussed. The simulator is implemented in bilingual; Math Lab and C++ are at the backend supplying data and theories while Java handles all front-end GUI and action pattern updating. To compare the usefulness of the proposed framework, several experiments were conducted and the results were obtained by using visual features only (77.89% for precision; 72.10% for recall), audio features only (62.52% for precision; 48.93% for recall) and combined audiovisual (90.35% for precision; 90.65% for recall).
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
In this paper we present the implementation of speaker identification system using artificial neural network
with digital signal processing. The system is designed to work with the text-dependent speaker
identification for Bangla Speech. The utterances of speakers are recorded for specific Bangla words using
an audio wave recorder. The speech features are acquired by the digital signal processing technique. The
identification of speaker using frequency domain data is performed using backpropagation algorithm.
Hamming window and Blackman-Harris window are used to investigate better speaker identification
performance. Endpoint detection of speech is developed in order to achieve high accuracy of the system.
Utterance Based Speaker Identification Using ANNIJCSEA Journal
In this paper we present the implementation of speaker identification system using artificial neural network with digital signal processing. The system is designed to work with the text-dependent speaker identification for Bangla Speech. The utterances of speakers are recorded for specific Bangla words using an audio wave recorder. The speech features are acquired by the digital signal processing technique. The identification of speaker using frequency domain data is performed using back propagation algorithm. Hamming window and Blackman-Harris window are used to investigate better speaker identification performance. Endpoint detection of speech is developed in order to achieve high accuracy of the system.
Utterance Based Speaker Identification Using ANNIJCSEA Journal
In this paper we present the implementation of speaker identification system using artificial neural network with digital signal processing. The system is designed to work with the text-dependent speaker identification for Bangla Speech. The utterances of speakers are recorded for specific Bangla words using an audio wave recorder. The speech features are acquired by the digital signal processing technique. The identification of speaker using frequency domain data is performed using backpropagation algorithm. Hamming window and Blackman-Harris window are used to investigate better speaker identification performance. Endpoint detection of speech is developed in order to achieve high accuracy of the system.
Acoustic Scene Classification by using Combination of MODWPT and Spectral Fea...ijtsrd
Acoustic Scene Classification ASC is classified audio signals to imply about the context of the recorded environment. Audio scene includes a mixture of background sound and a variety of sound events. In this paper, we present the combination of maximal overlap wavelet packet transform MODWPT level 5 and six sets of time domain and frequency domain features are energy entropy, short time energy, spectral roll off, spectral centroid, spectral flux and zero crossing rate over statistic values average and standard deviation. We used DCASE Challenge 2016 dataset to show the properties of machine learning classifiers. There are several classifiers to address the ASC task. We compare the properties of different classifiers K nearest neighbors KNN , Support Vector Machine SVM , and Ensembles Bagged Trees by using combining wavelet and spectral features. The best of classification methodology and feature extraction are essential for ASC task. In this system, we extract at level 5, MODWPT energy 32, relative energy 32 and statistic values 6 from the audio signal and then extracted feature is applied in different classifiers. Mie Mie Oo | Lwin Lwin Oo "Acoustic Scene Classification by using Combination of MODWPT and Spectral Features" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27992.pdfPaper URL: https://www.ijtsrd.com/computer-science/multimedia/27992/acoustic-scene-classification-by-using-combination-of-modwpt-and-spectral-features/mie-mie-oo
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
1. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 4, April 2013
1347
www.ijarcet.org
Abstract— Nowadays, there are many kind of video such as
educational movies, multimedia movies, action movies and
scientific movies etc. The careful analysis and classification of
movies are very important. For example, movies containing
violent or profanity by putting a separate class as they are not
suitable for children can cut and avoid from movies for
watching. This system is proposed to provide indexing and
retrieving the most interesting and important events of the
action movie to attract the viewer by using the classified audio
categories. The propose system is extracted audio features and
make the model by using audio feature vector and classify the
audio class to detect and recognize video scenes. In this proposed
system, SVM is combined with HMM based on audio features
which often directly reflects while image information may not
yield useful “action” indexes, to be in detection scene by labeling
happy scene, miserable scene and action scene etc. Sound event
types are classified including gunshot, scream, car-breaking,
people talking, laughter, fighting, shouting and crowd
background.
Index Terms—— Audio Indexing, Feature Extraction,
Hidden Markov Model (HMM), Support Vector Machine
(SVM).
I. INTRODUCTION
Due to the advances of information technology, more and
more digital audio, images and video are being captured,
produced and stored. There are strong research and
development interests in multimedia database in order to
effectively and efficiently use the information stored in these
media types. The research effort of the past few years has
been mainly focused on indexing and retrieval of digital
images and video.
In video retrieval, the most common use of audio
information is for automatic speech recognition and the
subsequent use of the generated transcript for text retrieval.
However, the audio information can also be used, more
directly, to provide additional information such as the gender
of the speaker, music and speech separation and audio
textures such as fast speaking sports announcers. So that the
applications describing the content and the applications using
the corresponding descriptions can interoperate, it is
necessary to define a standard that specifies the syntax and
Manuscript received April, 2013.
K. M. Chit is with the University of Technology, Yatanarpon Cyber City,
PyinOoLwin, Myanmar.
K. Z. Lin was with the University of Technology, Yatanarpon Cyber
City, PyinOoLwin, Myanmar. She is now with the Department of Hardware
Technology, University of Computer Studies, Yangon, Bahan Campus,
Myanmar.
semantics of these multimedia descriptions.
Moreover, classification of video clips will help to solve
the problem of managing and accessing huge amount of video
data in these days. A video sequence is a rich information
source, containing audio, and text and image objects.
Although the human being can understand the semantic
content by fusing the information from different modalities,
computer understanding of video sequence is still in a quite
poor state. In the past several years most of the research has
focused on the use of speech and image information. These
include the use of speech recognition and language
understanding techniques to produce keywords for each video
frame or a group of frames.
However, visual features may cause semantically unrelated
shots to be clustered into one unit only because they may be
similar. Recently several researchers have started to classify
video scene by using audio signal because audio-based
analysis requires significantly less computation and the audio
information contain a number of clues about semantics of the
video data.
In this paper, a system is proposed to provide the video
scene annotation by classifying the audio classes in action
movies. The best audio features are selected for audio classes
by modeling HMM and SVM classifier is used to categorize
the mixed types of audio. This approach is to provide scene
recognition based on separating audio class and to get the
better performance of video scene detection. In addition it is
to obtain better accuracy for sound classification.
The rest of this paper is organized as follows. In Section II,
we present the related work and Section III describes how an
audio clip is represented by low level perceptual and cepstral
feature and gives and overviews of linear, kernel SVM and
HMM. In Section IV, proposed algorithm is explained for
classification and in Section V, experimental evaluation is
presented. Finally, in Section VI, we conclude for the
proposed system.
II. RELATED WORK
The classification of audio signals using SVM and
RBFNN was proposed by P. Dhanalakshmi and S. Palanivel
[1]. Linear predictive coefficients, linear predictive cepstral
coefficients and mel-frequency cepstral coefficients audio
features are calculated as features to characterize audio
content. Support vector machines are applied to classify audio
into their respective classes by learning from training data.
Then the method extends the application of neural network
(RBFNN) for the classification of audio. S. Jain and R.S.
Audio-Based Action Scene Classification Using
HMM-SVM Algorithm
Khin Myo Chit, K Zin Lin
3. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 4, April 2013
1349
www.ijarcet.org
xi Є Rn is a feature vector and yi Є {−1, +1} is a class label,
with a separating hyper-plane of equation w·x+bv = 0; of all
the boundaries determined by w and b. On the basis of this
rule, the final optimal hyper-plane classifier can be
represented by the following equation:
1
( ) sgn( )
l
i i
i
f x y x x b
(5)
where α and b are parameters for the classifier; the solution
vector xi is called as Support Vector with αi being non-zero.
In the linearly non-separable but non-linearly separable case,
the SVM replaces the inner product by a kernel function
K(x,y), and then constructs an optimal separating hyper-plane
in the mapped space.
According to the Mercer theorem, the kernel function
implicitly maps the input vectors into a high dimensional
feature space in which the mapped data is linearly separable.
In this method, the Gaussian Radial Basis kernel will be used:
2
2
( , ) exp(
2
i j
i j
x x
K x x
(6)
where the output of the kernel is dependent on the
Euclidean distance of xj from xi (one of these will be the
support vector and the other will be the testing data point).
The support vector will be the center of the RBF and σ will
determine the area of influence this support vector has over
the data space.
IV. PROPOSED SYSTEM
Recent advances in multimedia compression technology,
the significant increase in computer performance and the
growth of Internet, have led to the widespread use and
availability of digital video. The availability of audio-visual
data in the digital format is increasing day by day. Currently
several web sites host movies and provide users with the
facility to browse and watch online movies. Movies constitute
a large portion of the entertainment industry and they are
always preceded by previews (trailers) and promotional
videos because of the commercial nature of movie
productions. So, several researchers have started to
investigate the potential of analyzing audio signal for video
scene classification.
In this proposed system, only audio information is used to
separate the different audio class because audio-based
analysis requires significantly less computation, it can be used
in a preprocessing stage before more comprehensive analysis
involving visual information. To provide automatic scene
extraction models, audio data is considered in order to make
the extraction results closer to human understanding and
audio in fact tells a lot about mood of the clip, the music
component, the noise, fast or slowness of the pace and the
human brain too can classify just on the base of audio.
The motivation of combining the HMM and SVM is to
combine the strong generalization ability of the SVM and the
output probability of HMM for reducing audio features
dimension by modeling to select the best features for each
class.
In this work, an audio clip is classified into one of eight
classes. The first step of the classification is to extract audio
track from video file for feature calculation. To convey the
information on extracted audio file, the audio quality is put
with the sampling frequency of 22 kHz, bit rate of 128 kbps
and mono channel.
The purpose of the second stage is to extract features and
analyze in two levels: frame-level and clip-level by using
audio features ZCR, STE, VRMS, VDR and MFCC which are
proved to be effective for distinguishing audio classes. In this
stage, the audio stream is analyzed into 1sec audio clips with
0.5 sec overlap and each clip is divided into frames of 20ms
with non-overlapping.
Before performing the actual data mining process, for the
sake of accuracy and efficiency, a pre-filtering process is
needed to clean data. To represent a clip, a total of 17 features
are extracted from each clip. Although all these features can
be used to distinguish audio, some features may contain more
information than others. Using only a small set of the most
powerful features will reduce the time for feature extraction
and classification. Moreover, the existing research has shown
that when the number of training sample is limited, using a
large feature set may decrease the generality of a classifier.
Therefore, each audio class is modeled by using HMM to get
the clean data and effective features. Extracted audio features
using HMM are in the same dimension and in the same vector
space. So reducing feature dimension by using HMM model
can help the SVM to get the best training data and to raise the
classification speed and accuracy while SVM classify the
audio classes. In summary, the pseudo code of HMM-SVM
algorithm is shown in Fig. 1.
HMM-SVM Algorithm
1. Support that there are k training set expressed as
L={L1,….., L2}.
2. Each training set corresponds to an HMM set λ={λ1,……,
λk}, when λi={λ1,……, λki}.
3. Calculate the probability P(O ǀ λ), the maximum
probability of a class Lj in this set is
1
max ( )
j Ni
P O j
.
4. From HMM algorithm, get new training data set.
5. Run the SVM algorithm by using new training data sets
from HMM.
Fig.1. HMM-SVM algorithm
The overall procedure of the proposed system is followed:
1. Collect the audio files from video files.
2. Extract the features from audio files by using ZCR, STE,
VRMS, VDR and MFCC.
3. Select the best features for each class by using HMM
model.
4. Get the best training dataset from the above step.
5. Train the data for SVM classifier.
6. Classify the audio classes by using SVM.
7. Calculating the performance and accuracy of proposed
system.
The proposed system design is shown in Fig.2.
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International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 4, April 2013
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www.ijarcet.org
can reduce the classification errors and it can detect events
from other games, or even other type of videos. This system is
designed to fulfill the requirement of video viewer and to
improve the dealings with video for home user.
Ongoing work of this research is to detect and recognize
video scene by using the audio classes. The resulting audio
classes are applied to event detection and provide action
scene annotation based on separating audio class. Other audio
classes such as siren, horn, running water, thundering and bird
sound are considered in future to skip commercial shots and
locate the shot or story that users are interested in.
ACKNOWLEDGMENT
I am very grateful to Dr. K Zin Lin for fruitful discussion
during the preparation of this paper and also specially thank to
Rector Dr Aung Win, Professors and all my teachers from
Technology University (Yatanarpon Cyber City), Myanmar.
REFERENCES
[1] P. Dhanalakshmi and S. Palanivel, “Classification of audio signals
using SVM and RBFNN,” Journal of Expert Systems with
Applications, vol. 36, pp. 6069-6075, 2008.
[2] R. S. Selva Kumari, D. Sugumar and V. Sadasivam, “Audio signal
classification based on optimal wavelet and support vector machine,”
IEEE, International Conference on Computational Intelligence and
Multimedia Applications (ICCIMA), vol. 2, pp. 544-548, Dec 2007.
[3] S. Jain and R.S. Jadon, “Audio based movies characterization using
neural network,” International Journal of Computer Science and
Applications, vol. 1, no. 2, pp. 87-90, August 2008.
[4] S. Chu, S. Narayanan, and C.-C. J. Kuo, “Environmental sound
recognition with time-frequency audio features,” IEEE Trans. on
Audio, Speech, and Language Processing, vol. 17, no. 6, pp.
1142–1158, August 2009.
[5] S.-G. Ma and W.-Q. Wang, “Effectively discriminating fighting shots
in action movies,” Journal of Computer Science and Technology,
IEEE, vol. 26, no. 1, pp. 187-194, Jan 2011.
[6] T. Zhang and C.-C. Kuo, “Audio content analysis for online
audiovisual data segmentation,” IEEE Trans. On Speech and Audio
Processing, vol. 9, no. 4, pp. 441-457, May 2011.
[7] V. Elaiyaraja and P. M. Sundaram, “Audio classification using support
vector machines and independent component analysis,” Journal of
Computer Applications (JCA), vol. 5, issue. 1, 2012.
K. M. Chit received Bachelor of Engineering from Technological
University (Kyaukse), Mandalay in Myanmar. She completed the Master
course from Mandalay Technological University since 2010 and especially
studied and finished the Thesis by Networking. She is working now in
Technological University (Taunggyi) as at Assistant Lecturer under
Information Technology Department. Now, She is a Ph.D student in
University of Technology (Yatanarpon Cyber City) near PyinOoLwin,
Upper Myanmar. Her fields of interest are Multimedia Signal Processing,
Audio Indexing and Audio Information Retrieval.
Fig. 2. Proposed System Design
T= [P(O | λt )]
SVMn
T= [P(O | λt )]
P(O | λJ )
P(O | λ2 )
P(O | λ1 )
P(O | λ1 )
P(O | λJ )
P(O | λ2 )
SVM1
Scream
Audio
files
Extracted
features Select
maximum
training
data set
Probability
computation λ1
.
.
.
Probability
computation λ2
Probability
computation λ j
HMM for
Pre-training data set
Select
maximum
training
data set
Probability
computation λ1
.
.
.
Probability
computation λ2
Probability
computation λ j
HMM for
Pre-training data set
.
.
.
.
.
.
.
Video
Gun
Shot