This slide presents a project on detecting music genres using Machine Learning algorithm HMM using Python. More on this project is available on my Github.
Object classification using CNN & VGG16 Model (Keras and Tensorflow) Lalit Jain
Using CNN with Keras and Tensorflow, we have a deployed a solution which can train any image on the fly. Code uses Google Api to fetch new images, VGG16 model to train the model and is deployed using Python Django framework
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Object classification using CNN & VGG16 Model (Keras and Tensorflow) Lalit Jain
Using CNN with Keras and Tensorflow, we have a deployed a solution which can train any image on the fly. Code uses Google Api to fetch new images, VGG16 model to train the model and is deployed using Python Django framework
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Although manipulations of visual and auditory media are as old as the media themselves, the recent entrance of deepfakes has marked a turning point in the creation of fake content. Powered by latest technological advances in AI and machine learning, they offer automated procedures to create fake content that is harder and harder to detect to human observers. The possibilities to deceive are endless, including manipulated pictures, videos and audio, that will have large societal impact. Because of this, organizations need to understand the inner workings of the underlying techniques, as well as their strengths and limitations. This article provides a working definition of deepfakes together with an overview of the underlying technology. We classify different deepfake types: photo (face- and body-swapping), audio (voice-swapping, text to speech), video (face-swapping, face-morphing, full body puppetry) and audio & video (lip-synching), and identify risks and opportunities to help organizations think about the future of deepfakes. Finally, we propose the R.E.A.L. framework to manage deepfake risks: Record original content to assure deniability, Expose deepfakes early, Advocate for legal protection and Leverage trust to counter credulity. Following these principles, we hope that our society can be more prepared to counter the deepfake tricks as we appreciate its treats.
This ppt contains basic idea of deep learning, motivation behind deep learning, technical overview of AI and deep learning, types of learning approaches, basic DL model development steps and at last applications of deep learning including various areas where deep learning can prominently applied.
One of the determinants for a good anomaly detector is finding smart data representations that can easily evince deviations from the normal distribution. Traditional supervised approaches would require a strong assumption about what is normal and what not plus a non negligible effort in labeling the training dataset. Deep auto-encoders work very well in learning high-level abstractions and non-linear relationships of the data without requiring data labels. In this talk we will review a few popular techniques used in shallow machine learning and propose two semi-supervised approaches for novelty detection: one based on reconstruction error and another based on lower-dimensional feature compression.
An Autoencoder is a type of Artificial Neural Network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise.”
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Although manipulations of visual and auditory media are as old as the media themselves, the recent entrance of deepfakes has marked a turning point in the creation of fake content. Powered by latest technological advances in AI and machine learning, they offer automated procedures to create fake content that is harder and harder to detect to human observers. The possibilities to deceive are endless, including manipulated pictures, videos and audio, that will have large societal impact. Because of this, organizations need to understand the inner workings of the underlying techniques, as well as their strengths and limitations. This article provides a working definition of deepfakes together with an overview of the underlying technology. We classify different deepfake types: photo (face- and body-swapping), audio (voice-swapping, text to speech), video (face-swapping, face-morphing, full body puppetry) and audio & video (lip-synching), and identify risks and opportunities to help organizations think about the future of deepfakes. Finally, we propose the R.E.A.L. framework to manage deepfake risks: Record original content to assure deniability, Expose deepfakes early, Advocate for legal protection and Leverage trust to counter credulity. Following these principles, we hope that our society can be more prepared to counter the deepfake tricks as we appreciate its treats.
This ppt contains basic idea of deep learning, motivation behind deep learning, technical overview of AI and deep learning, types of learning approaches, basic DL model development steps and at last applications of deep learning including various areas where deep learning can prominently applied.
One of the determinants for a good anomaly detector is finding smart data representations that can easily evince deviations from the normal distribution. Traditional supervised approaches would require a strong assumption about what is normal and what not plus a non negligible effort in labeling the training dataset. Deep auto-encoders work very well in learning high-level abstractions and non-linear relationships of the data without requiring data labels. In this talk we will review a few popular techniques used in shallow machine learning and propose two semi-supervised approaches for novelty detection: one based on reconstruction error and another based on lower-dimensional feature compression.
An Autoencoder is a type of Artificial Neural Network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise.”
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Machine listening is a field that encompasses research on a wide range of tasks, including speech recognition, audio content recognition, audio-based search, and content-based music analysis. In this talk, I will start by introducing some of the ways in which machine learning enables computers to process and understand audio in a meaningful way. Then I will draw on some specific examples from my dissertation showing techniques for automated analysis of live drum performances. Specifically, I will focus on my work on drum detection, which uses gamma mixture models and a variant of non-negative matrix factorization, and drum pattern analysis, which uses deep neural networks to infer high-level rhythmic and stylistic information about a performance.
Shoichi Koyama, Naoki Murata, and Hiroshi Saruwatari. "Super-resolution in sound field recording and reproduction based on sparse representation"
presented at 5th Joint Meeting Acoustical Society of America and Acoustical Society of Japan (28 Nov. - 2 Dec. 2016, Honolulu, USA)
Human Perception and Recognition of Musical Instruments: A ReviewEditor IJCATR
Musical Instrument is the soul of music. Musical Instrument and Player are the two fundamental component of Music. In
the past decade the growth of a new research field targeting the Musical Instrument Identification, Retrieval, Classification,
Recognition and management of large sets of music is known as Music Information Retrieval. An attempt to review the methods,
features and database is done.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
2. Literature Survey
WAV File format
■ Waveform Audio File Format (WAVE, or more commonly known asWAV due to
its filename extension) is a Microsoft and IBM audio file format standard for
storing an audio bitstream on PCs.
■ It is the main format used onWindows systems for raw and typically
uncompressed audio.The usual bitstream encoding is the linear pulse-code
modulation (LPCM) format.
3. Mel Frequency Cepstral Coefficient (MFCC)
■ In sound processing, the mel-frequency cepstrum (MFC) is a representation of the short-
term power spectrum of a sound
■ Mel-frequency cepstral coefficients (MFCCs) are coefficients that collectively make up an
MFC
■ MFCCs are commonly derived as follows:
– Take the Fourier transform of (a windowed excerpt of) a signal.
– Map the powers of the spectrum obtained above onto the mel scale, using triangular
overlapping windows.
– Take the logs of the powers at each of the mel frequencies.
– Take the discrete cosine transform of the list of mel log powers, as if it were a signal.
– The MFCCs are the amplitudes of the resulting spectrum.
4. Audio genres
■ BLUES:
– The blues form is a cyclic musical form in which a repeating progression of chords
mirrors the call and response scheme commonly found in African and African-
American music.Typical instruments are Guitar, bass guitar, piano, harmonica,
upright bass, drums, saxophone, vocals, trumpet, cornet, trombone
■ CLASSICAL:
– The Classical era stringed instruments were the four instruments which form the
string section of the orchestra: the violin, viola, cello and contrabass.Woodwinds
included the basset clarinet, basset horn, the Classical clarinet, the chalumeau, the
flute, oboe and bassoon. Keyboard instruments included the clavichord and the
fortepiano
5. ■ JAZZ:
– It is music that includes qualities such as swing, improvising, group interaction,
developing an 'individual voice', and being open to different musical possibilities.
Typical instruments are Double bass, drums, guitar (typically electric guitar), piano,
saxophone, trumpet, clarinet, trombone, vocals, vibraphone, Hammond organ and
harmonica. In jazz fusion of the 1970s, electric bass, electric piano and synthesizer
were common.
■ ROCK:
– Rock music is a genre of popular music that originated as "rock and roll" in the
United States in the 1950s, and developed into a range of different styles in the
1960s and later, particularly in the United Kingdom and the United States.Typical
instruments are Electric guitar, bass guitar, acoustic guitar, drums, piano,
synthesizer, keyboards
6. Collection of dataset
■ We have considered four genres for this project
– Jazz
– Rock
– Classical
– blues
7. HIDDEN MARKOV MODEL (HMM)
■ A hidden Markov model (HMM) is a statistical Markov model in which the system
being modeled is assumed to be a Markov process with unobserved (hidden) states.A
HMM can be presented as the simplest dynamic Bayesian network.
■ In a hidden Markov model, the state is not directly visible, but output, dependent on
the state, is visible. Each state has a probability distribution over the possible output
tokens.
■ The sequence of tokens generated by an HMM gives some information about the
sequence of states.
■ The adjective 'hidden' refers to the state sequence through which the model passes,
not to the parameters of the model; the model is still referred to as a 'hidden' Markov
model even if these parameters are known exactly.
8. HMM
■ if the n'th observation in a chain of observations is influenced by a corresponding
latent (i.e. hidden) variable ie.., If the latent variables are discrete and form a Markov
chain, then it is a hidden Markov model (HMM)
9. PROBLEM STATEMENT
■ Music can be classified into genres based on the style it follows
■ The classification of music is an important part as a person’s taste in music is limited to
a few genres, personalized to them
■ Due to the boom in information, huge amount of music data can be found without
proper description or classification
■ This project aims at categorizing music samples into four genres – jazz, blues, rock,
classic
10. Steps involved
■ Collection of dataset
■ Preprocessing of dataset
■ Training of classifier
■ Classification of music samples
11. Collection of dataset
■ Each of these genres have distinctive characteristics
■ Music samples for each of these genres have been collected
■ Each genre has 100 music samples
■ This number of samples have been considered for a better efficiency of the classifier
■ The ratio considered for training and testing is 3:1 ie.., 75% samples will be used for
training and 25%, for testing
■ Out of 400 samples, 300 samples will be used for training and 100 samples will be used
for testing
13. IMPLEMENTATION
■ PRE-PROCESSING STAGE
– This module automates the audio conversion process.This module scans the current
directory to find all the mp3 files.Then, each file is loaded one by one. An audio clip
may be one-channeled or two channeled.The final step in this automated process is
to decompress the audio and store it in wave format, which is a lossless compression
format. After the process is complete, the current directory contains all wave files.
This process has to be repeated for every genre tag being considered.
start
get list of mp3 files in pwd
for every item in the list do :
load file
set # of channels to 1
export as wave
end
14. ■ FEATURE EXTRACTION STAGE
– The python_speech_features library provides common speech features for ASR
including MFCCs and filterbank energies.
■ features.mfcc() - Mel Frequency Cepstral Coefficients
■ features.logfbank() - Log Filterbank Energies
■ CLASSIFICATION STAGE
– Classifier used is Hidden markov model
– Four HMMs used to classify into 4 genres
– Maximum probability predicts genre of music
15. TESTINGAND SCREEN SHOTS
■ TESTING STAGE
– Input sample gives probability for each genre when it is tested with HMM classifier
– 30% of dataset is used for testing classifier
– Confusion matrix is as given
16. RESULTSAND DISCUSSION
■ Training was done with two sizes of datasets-
– Containing 400 audio files
– Containing 100 audio files
■ For bigger dataset, higher efficiency was achieved
■ For smaller dataset, lesser efficiency was achieved.
■ The genres of blues and rock have similar elements, and therefore some of the
samples were classified into the other category, and hence the overall efficiency of
smaller datasets were reduced
17. CONCLUSION
■ The Hidden markov model approach for music genre classification has an efficiency of
around 72% when the dataset size considered for training is huge.
18. FUTUREWORK
■ GUI for the application
■ Input file from user
■ Include more classes
■ Display classification and find if audio sample belong to more than one genre
19. REFERENCES
[1] Molau, Pitz, Schlueter, Ney :Computing Mel- Frequency Cepstral Coefficients
on the Power Spectrum, RWTH Aachen – University ofTechnology, 52056
Aachen,Germany. IEEETransactions on Communications 26 (6)
[2] Frigo, M.; Johnson, S. G. (2005). "The Design and Implementation of FFTW3"
(PDF). Proceedings of the IEEE 93: 216–231. 10.1109/jproc.2004.840301
[3] Narasimha, M.; Peterson, A. (June 1978). "On the Computation of the
Discrete CosineTransform". IEEETransactions on Communications 26 (6): 934–
936. 10.1109/TCOM.1978.1094144
20. [4] S. B. Kotsiantis : Supervised Machine Learning:A Review of Classification
Techniques, 2007, University of Peloponnesse,Greece
[5] DavidA. Freedman (2009). Statistical Models:Theory and Practice.
Cambridge University Press. p. 128.
[6] Musical genre classification of audio signals, Speech and Audio Processing,
IEEETransactions on (Volume:10 , Issue: 5 ), 293-302, November 2002,
http://dspace.library.uvic.ca:8080/bitstream/handle/1828/1344/tsap02gtzan.pdf
21. ■ WEB LINKSAND OTHER RESOURCES
[7] http://willdrevo.com/fingerprinting-and-audio-recognition-with-python/
[8] en.wikipedia.org/wiki/Hidden_Markov_Model
[9] en.wikipedia.org/wiki/Mel_Frequency_Ceptral
[10] en.wikipedia.org/wiki/Window_function
[11] http://practicalcryptography.com/miscellaneous/machine-learning/guide-
mel-frequency-cepstral-coefficients-mfccs/