#matlab and octave

[tom2tec]

Playrec ~ Multi-channel Matlab Audio

Playrec is a Matlab and Octave utility (MEX file) that provides simple yet versatile access to soundcards using PortAudio, a free, open-source audio I/O library. It can be used on different platforms (Windows, Macintosh, Unix) and access the soundcard via different host API including ASIO, WMME and DirectSound under Windows. www.playrec.co.uk

[eslamdahab-dhb]

Look at open-source software.

Here are some examples:

Ableton Live ➔ Reaper, LMMS, Ardour

Adobe After Effects ➔ Natron

Adobe Animate ➔ OpenToonz, Tahoma2D, Pencil2D

Adobe Audition ➔ Audacity

Adobe Dreamweaver ➔ Blue Fish

Adobe Illustrator ➔ Inkscape

Adobe InDesign ➔ Scribus

Adobe Lightroom ➔ Darktable

Adobe Photoshop ➔ GIMP, Krita

Adobe Premier ➔ Olive, Lightworks, Kdenlive, Shotcut

AutoCAD ➔ OpenSCAD, LibreCAD, FreeCAD

Corel Draw ➔ Inkscape

Corel Painter ➔ Krita, MyPaint

Matlab, Mathematica, Maple ➔ GNU Octave, Scilab, FreeMath, wxMaxima, Sage Math, GeoGebra Classic

Maya, 3dsMax ➔ Blender

Microsoft Excel ➔ LibreOffice Calc

Microsoft Office ➔ LibreOffice, OnlyOffice Desktop Editors

Microsoft Powerpoint ➔ LibreOffice Impress

Microsoft Visio ➔ Dia, LibreOffice Draw

Microsoft Windows ➔ Linux Mint, Pop!_OS, and any other Linux distribution

Microsoft Word ➔ LibreOffice Writer

OneNote ➔ Joplin, XJournal++, Zim

Outlook ➔ Thunderbird

Pinnacle Studio or any Video Editing Software ➔ Kdenlive, Shotcut

Unity, Unreal, GameMaker ➔ Godot, Love2D

[call-me-rucy]

Wdym there's not an implementation of this stupidly specific algorythm in this programing language that almost no one uses.

why doesnt adapthisteq work on octave? Long shot but does anyone perchance have at hand an implemantation of CLAHE that runs in Octave without errors and in less than a minute???

Cause nooooo we couldnt do this in Matlab, or R, or even python...

[eagletflysolutions]

Machine Learning Training Online

You need to learn it to train machines like humans through trial-and-error methods. There are multiple other things you need to learn, including how reinforcement learning works, Markov Decision Processes Q-learning algorithm. Then our machine learning course syllabus will give you a structured outline of subjects and topics to learn. Collaborates with leading educational organizations to expand the reach of deep learning training to developers worldwide - online data science course.

Machine Learning courses are basically outlined at developed and novice levels and may be pursued at the student's convenience and against a particular enrolment cost. While not all the courses require you to learn Python, C++,  having some familiarity and background with these programming languages is worthwhile. Originating from the IPython project, it offers a comprehensive framework for interactive computing, including notebooks, code, and data management.

What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, etc. Machine Learning is a pathway to the most exciting careers in data analysis today - Best data science institute in delhi.

Machine Learning course, available on our online website, is a popular course option that has introduced countless learners to the world of machine learning. As one of the pioneering online courses in the field, it provides a comprehensive introduction to machine learning algorithms, including supervised and unsupervised learning, and deep learning. Learners get hands-on experience with Octave/MATLAB for coding assignments and gain a solid foundation in machine learning theory. For more information, please visit our site https://eagletflysolutions.com/ 

[codeshive]

DT2118 Lab2: Hidden Markov Models with Gaussian Emissions solved

1 Objective The objective is to implement the algorithms for evaluation and decoding of Hidden Markov Models (HMMs) combined with Gaussian emission probability distributions. The lab is designed in Python, but the same functions can be obtained in Matlab/Octave or using the Hidden Markov Toolkit (HTK). 2 Task The overall task is to implement and test methods for isolated word recognition: •…

[codingprolab]

DT2118 Lab2: Hidden Markov Models with Gaussian Emissions

1 Objective The objective is to implement the algorithms for evaluation and decoding of Hidden Markov Models (HMMs) combined with Gaussian emission probability distributions. The lab is designed in Python, but the same functions can be obtained in Matlab/Octave or using the Hidden Markov Toolkit (HTK). 2 Task The overall task is to implement and test methods for isolated word recognition: •…

[myprogrammingsolver]

Assignment #1 Solved

A major purpose of this assignment is to familiarize you with MATLAB (or OCTAVE, PYTHON, JULIA, etc.; see the “Programming languages” handout). You will need to find, download, or purchase a copy. Make sure you can open and close the command window, use it as a calculator, and plot simple things. Make sure you can create a new program (text file), save it, edit it, and run it at the command…

[mafaweb]

Ch interpreter nedir ve nasıl çalışır? Ch interpreter, C programlama dili için bir yorumlayıcıdır ve kodun hızlı ve etkili bir şekilde çalıştırılmasını sağlar. Yüksek düzeyli bir dilden makine diline çevrim yapmadan doğrudan çalıştırılabilen bir programdır. Ch interpreter, ANSI C standartlarını destekler ve birçok farklı platformda kullanılabilir. Sisteminize uygun olan derleyici programını kullanarak yazdığınız kodu direkt olarak çalıştırabilirsiniz. Ch interpreter kullanmanın avantajları arasında, hızlı ve kolay kullanım, taşınabilirlik, derleme süresinin yokluğu ve kodu hızlıca test etme imkanı bulunmaktadır. Ch interpreter, kullanıcıların C programlama dilinde yazdıkları kodları hızlı bir şekilde çalıştırarak, yazdıkları programların hata ayıklama sürecini kolaylaştırır ve zaman kazandırır. Ch interpreter hangi programlama dillerini destekler?Ch interpreter, C dilinin bir yorumlayıcısıdır ve birçok farklı programlama dili için destek sunar.Ch interpreter C, C++, Fortran, OpenGL, Matlab, Octave, Scilab, Perl, Ruby, Python, and Tk gibi birçok programlama dili ve kütüphanesiyle uyumlu çalışabilir.Aynı zamanda, Ch interpreter tüm POSIX ile uyumlu sistemlerle ve popüler işletim sistemleriyle uyumludur.Bu özellikleri sayesinde, Ch interpreter geniş bir programlama dilini ve sistemini destekleyerek kullanıcılarına çeşitlilik ve esneklik sunar.Ch interpreter kullanmanın avantajları nelerdir?Ch interpreter, farklı programlama dilleri ile yazılmış olan kodları derlemek ve çalıştırmak için kullanılan bir araçtır. Bu yazılımın kullanılmasının birçok avantajı bulunmaktadır. İlk olarak, Ch interpreter çok çeşitli programlama dillerini destekler. Bu sayede, farklı dilde yazılmış kodları tek bir ortamda çalıştırabilirsiniz.Diğer bir avantajı ise hata ayıklama işlemlerinin kolaylığıdır. Ch interpreter ile hatalı kodun nerede olduğunu bulmak ve düzeltmek çok daha kolaydır. Ayrıca, kodun derlenip çalıştırılması esnasında ortaya çıkan hataları da detaylı bir şekilde görebilirsiniz.Bununla birlikte, Ch interpreter kullanarak hızlı prototipler oluşturabilirsiniz. Kodunuzu hızlı bir şekilde çalıştırarak, fikirlerinizi test etmek ve geliştirmek için uygun bir ortam sağlar.Ayrıca, Ch interpreter kodunuzu anlık olarak değiştirme imkanı sunar. Bu da geliştirme sürecinizin verimliliğini artırır ve zaman kazandırır.Ch interpreter'in özellikleri ve işlevleri nelerdir? Ch interpreter, C programlama dilini yorumlayan ve çalıştıran bir programdır. Bu program sayesinde C programlama dilinde yazılmış olan kodlar yorumlanarak çalıştırılabilir. Ch interpreter programı, karmaşık matematiksel hesaplamalar, veri analizi ve bilimsel çalışmalar gibi alanlarda kullanılmaktadır. Ayrıca, sistem programlama ve gömülü cihazlar için yazılım geliştirme sürecinde de sıklıkla tercih edilmektedir. Ch interpreter'ın özellikleri arasında gelişmiş derleme desteği, hızlı yürütme süresi ve geniş kütüphane seçenekleri bulunmaktadır. Ayrıca, dinamik tipli bir yapıya sahip olması ve farklı platformlarda çalışabilmesi de avantajlarındandır. Bununla birlikte, Ch interpreter programı, kullanıcıların kendi kütüphanelerini oluşturmasına olanak tanır ve farklı dillerle entegrasyonu kolaylaştırır. Ch interpreter ile nasıl program yazılır?Ch interpreter kullanarak program yazmak oldukça kolay bir işlemdir. İlk olarak, Ch interpreter'ı bilgisayarınıza indirip kurmanız gerekmektedir. Daha sonra, bir metin düzenleyici kullanarak Ch programlama dili ile kodlarınızı yazabilirsiniz.Ch dilinde bir program yazarken, değişkenler, operatörler, döngüler ve koşullu ifadeler gibi temel programlama kavramlarını kullanabilirsiniz. Bu sayede, çeşitli algoritmaları ve uygulamaları Ch interpreter ile kolaylıkla geliştirebilirsiniz.Bunun yanı sıra, Ch interpreter ile yazdığınız programları direkt olarak çalıştırabilir, hata ayıklama yapabilir ve çıktıları görüntüleyebilirsiniz. Bu da size programınızın doğru çalışıp çalışmadığını kolayca kontrol etme imkanı sağlar.Sonuç olarak, Ch interpreter ile program yazmak, hızlı, pratik ve verimli bir şekilde gerçekleştirilebilecek bir işlemdir.

Eğer temel programlama bilgileriniz varsa, Ch interpreter'ı kullanarak kolaylıkla programlama çalışmalarınızı sürdürebilirsiniz.Ch interpreter ile hata ayıklama nasıl yapılır?Ch interpreter ile hata ayıklama yapmak oldukça kolaydır. İlk olarak, programınızı çalıştırdığınızda, -g bayrağını kullanarak programınızı derlemeniz gerekir. Bu, hata ayıklama modunu etkinleştirecektir. Daha sonra, -o bayrağını kullanarak oluşturulan dosyanın adını belirtmelisiniz.Ardından, hata ayıklama işlemine başlamak için chdb komutunu kullanmalısınız. Bu, hata ayıklama konsolunu açacaktır. İşte bu konsol üzerinden adım adım programınızı çalıştırabilir ve hataları tespit edebilirsiniz.Hata ayıklama işlemi bittiğinde quit komutunu kullanarak konsolu kapatabilir ve programınızdaki hataları gidermek için düzeltmeler yapabilirsiniz. Son olarak, değişiklikleri kaydedip programınızı tekrar derleyerek hatalardan kurtulabilirsiniz.Ch interpreter ile hata ayıklama yapmak oldukça basit ve etkili bir yöntemdir. Bu sayede programınızda meydana gelen hataları daha hızlı bir şekilde tespit edebilir ve düzeltebilirsiniz.

[notationsworld]

Rang Lageya (Mohit Chauhan) - Sargam And Flute Notes

Rang Lageya (Mohit Chauhan) - Sargam, Harmonium And Flute Notes

                               SCALE OF THE FLUTE IS E BASS/MIDDLE Kya kahun aakhon ne meri sab keh diya P..m..G.. G..G..G..G..m..R..G..m..G.. Ik tu hi jeene ka matlab keh diya P..m..G.. G..G..G..G..m..R..G..m..G.. Hoo kya kahun aakhon ne meri sab keh diya P...P..m..G.. G..G..G..G..m..R..G..m..G.. Ik tu hi jeene ka matlab keh diya P..m..G.. G..G..G..G..m..R..G..m..G.. Ab saasein chalein na chalein kya fikar G..m..D..D..P..N..N..D..P..m..P..G.. Tu chale sath to khoobsurat safar G..m..D..D..P..N..N..D..P..m..P..G.. Teri raahon mein mera jahaan G..m..m..m..G..R..S..R.S.n. Ke rang lageya ishq da n..n..S..R..n..S..S..S..S..R...G.R.S.n. Ke rang lageya ishq da n..n..S..R..n..S..S..S..S...n..d..p.. Main tera hoya is tarah n..n..S..R..n..S..S..S..S..R...G.R.S.n. Ke rang lageya ishq da n..n..S..R..m..G..R..S..S... Also Read This :- - Pyar Hua Iqraar Hua Hai – Sargam, Harmonium And Flute Notes - Besharam Rang (Pathaan) – Sargam, Harmonium And Flute Notes - Lag Jaa Gale – Sargam, Harmonium And Flute Notes - Bana Sharabi – Sargam, Harmonium And Flute Notes - Tu Bhoola Jise (Airlift) – Sargam, Harmonium And Flute Notes Where DO -  SA       -    S RE  -  RE      -    R MI  -  GA      -    G FA  -   MA      -  M SO  -   PA         - P LA  -  DHA      - D TI    -  NI          - N LOW OCTAVE PA -  p DHA -  d NI -  n SHUDH MA - m        TIWAR MA - M HIGH OCTAVE SA -    S' RE -     R' GA -     G' MA -     M'   m' PA -       P' KOMAL SWAR DHA -  D(k) NI -       N(k) RE -       R(k) GA -      G(k) Read the full article

[krishna2revolt]

The Best Machine Learning Courses to Accelerate Your Career

Introduction

In the rapidly evolving world of technology, machine learning has emerged as a transformative force, revolutionizing industries and changing the way we interact with information. As businesses strive to harness the power of data-driven insights, the demand for skilled machine learning professionals is skyrocketing. To embark on a successful career in this field, enrolling in a high-quality machine learning course is crucial. In this article, we will explore some of the best machine learning courses available today.

"Machine Learning" by Stanford University (Coursera) Taught by the legendary Andrew Ng, this online course from Stanford University is widely regarded as a definitive introduction to machine learning. It covers the fundamental concepts, algorithms, and practical applications, making it an ideal starting point for beginners. The course employs a hands-on approach, with programming assignments in Octave or MATLAB, enabling students to implement their learning directly.

"Deep Learning Specialization" by deeplearning.ai (Coursera) Also led by Andrew Ng, this specialization is a must for those seeking to dive deep into the world of deep learning. It comprises five courses, covering neural networks, convolutional networks, recurrent networks, and more. This specialization offers a comprehensive understanding of state-of-the-art deep learning techniques and their applications in computer vision, natural language processing, and other fields.

"Machine Learning A-Z™: Hands-On Python & R In Data Science" (Udemy) This highly popular Udemy course provides an all-encompassing curriculum, catering to both Python and R users. With practical examples and real-world projects, learners gain valuable experience in applying machine learning algorithms to solve data science problems. The course covers regression, classification, clustering, and more, making it an excellent choice for individuals with diverse programming backgrounds.

"Applied Machine Learning" by Columbia University (edX) For those seeking a rigorous academic experience, this edX course from Columbia University is a perfect fit. It delves into advanced topics such as support vector machines, ensemble methods, and deep learning. The course includes hands-on labs using Python and scikit-learn, ensuring that learners develop a strong grasp of both theory and implementation.

"Machine Learning with TensorFlow on Google Cloud Platform" by Google Cloud (Coursera) This specialized course focuses on leveraging Google Cloud's machine learning services and TensorFlow to build powerful models. It covers topics like data preparation, feature engineering, model training, and hyperparameter tuning. Ideal for individuals looking to harness cloud-based machine learning solutions, this course equips learners with practical skills applicable to real-world projects.

"Fast.ai Practical Deep Learning for Coders" (fast.ai) Fast.ai is known for its unique approach to teaching deep learning, emphasizing a hands-on, practical methodology. This course empowers learners to quickly build and deploy cutting-edge deep learning models without extensive prerequisites. It covers topics like image classification, natural language processing, and collaborative filtering, among others, using popular libraries like PyTorch.

Conclusion

Machine learning continues to be at the forefront of technological innovation, shaping various industries and paving the way for a data-driven future. To excel in this dynamic field, choosing the right machine learning course is crucial. Whether you're a beginner or an experienced professional, the courses mentioned above offer top-notch instruction and hands-on experience to bolster your expertise and propel your career forward. Invest in your skills, embark on the journey of continuous learning, and unlock the limitless possibilities that machine learning has to offer.

[shieldfoss]

#By the way there's a free version of Matlab called octave

This was not true the last time I was told and went to check it out but, to be clear, the last time I checked was 2011 or thereabouts.

I'm all accepting and shit until I see a language that accepts single or double quotes for strings and I turn into a frothing lunatic; pick a fucking side you debauched reprobates

[codeshive]

DT2119 Lab1: Feature extraction solved

1 Objective The objective is to experiment with different features commonly used for speech analysis and recognition. The lab is designed in Python, but the same functions can be obtained in Matlab/Octave or using the Hidden Markov Toolkit (HTK). In Appendix A, a reference table is given indicating the correspondence between different systems. 2 Task • compute MFCC features step-by-step •…

[codingprolab]

DT2119 Lab1: Feature extraction

1 Objective The objective is to experiment with different features commonly used for speech analysis and recognition. The lab is designed in Python, but the same functions can be obtained in Matlab/Octave or using the Hidden Markov Toolkit (HTK). In Appendix A, a reference table is given indicating the correspondence between different systems. 2 Task • compute MFCC features step-by-step •…

[synths-and-sensibility]

Mmmmm... I don't know how AVR friendly this algorithm is as written, going to have to think about how to handle some of this stuff, get creative with integer arithmetic and all that...

Also, I decided to throw some code together for this in Octave (freeware MATLAB) for funsies and ooooh boy, I do not know how I feel about this language after not using it for the past 7ish years

Found an algorithm for a breakpoint envelope generator at the library. I already have the IC's on order for at least two of my EG ideas, so I'm excited to start prototyping some code.

[erikabsworld]

Top Resources for Acing Your Linear System Modeling Assignments

When it comes to mastering linear system modeling, having the right resources at your disposal can make all the difference. Whether you're just diving into the world of system analysis or looking to enhance your skills, a variety of tools and materials are available to help you succeed. In this blog post, we'll explore some of the top resources that can aid you in acing your linear system modeling assignments.

1. Textbooks and Online Guides

One of the fundamental resources for understanding linear system modeling is textbooks. Look for textbooks that are comprehensive, clear, and aligned with your course curriculum. Additionally, many online platforms offer guides and tutorials that provide supplementary explanations and examples to reinforce your learning. Websites like Khan Academy, MIT OpenCourseWare, and Coursera offer free or affordable resources that cover various aspects of linear system modeling.

2. Software and Simulation Tools

Practical application is key to mastering linear system modeling. Utilizing software and simulation tools allows you to experiment with different models and analyze their behavior in real-time. MATLAB, Simulink, and GNU Octave are popular choices among students and professionals alike for their versatility and robust features. These tools not only help you understand theoretical concepts but also prepare you for real-world engineering tasks.

3. Online Forums and Communities

Engaging with a community of peers can provide valuable insights and support as you tackle your linear system modeling assignments. Online forums like Stack Exchange, Reddit's r/EngineeringStudents, and MATLAB Central offer platforms for asking questions, sharing tips, and discussing challenges with fellow students and experts. Additionally, joining relevant LinkedIn groups or Discord servers can connect you with like-minded individuals and expand your network.

4. Academic Support Services

Sometimes, despite our best efforts, we encounter complex problems or time constraints that hinder our progress. In such cases, seeking assistance from academic support services can be beneficial. Whether it's attending office hours with your professor, joining study groups, or utilizing tutoring services offered by your university, don't hesitate to reach out for help when needed. Additionally, consider leveraging linear system modeling assignment help services for expert guidance and personalized assistance in completing your assignments efficiently.

Conclusion

Mastering linear system modeling requires dedication, practice, and access to the right resources. By incorporating textbooks, software tools, online communities, and academic support services into your learning arsenal, you can enhance your understanding and excel in your assignments. Remember, success is not just about individual effort but also about knowing when and where to seek help when faced with challenges. So, arm yourself with these top resources and embark on your journey to acing your linear system modeling assignments.