This document discusses using recurrent neural networks and long short-term memory networks to generate music. It notes that producing music can be expensive, but an AI system could provide a cheaper alternative for businesses. The system would be trained on music theory concepts like notes, chords, scales and keys to understand harmonious combinations. A web-based platform could then generate custom music based on user selections and input the trained machine learning model. The goal is an affordable way for companies to automatically produce unique music for branding and promotions.
The document discusses developing a model to compose monophonic world music using deep learning techniques. It proposes using a bi-axial recurrent neural network with one axis representing time and the other representing musical notes. The network will be trained on a dataset of MIDI files describing pitch, timing, and velocity of notes. It will also incorporate information from music theory on scales, chords, and other elements extracted from sheet music files. The goal is to generate unique musical sequences while adhering to music theory rules. The model aims to address the problem of composing long durations of background music for public spaces in an automated way.
This document discusses a method for extracting vocals from songs and converting them to instrumental covers using deep learning techniques. It involves using the Spleeter library to separate vocals from music tracks. The extracted vocals can then be converted to instrumental covers for different instruments using a DDSP (Differentiable Digital Signal Processing) library combined with pretrained convolutional neural networks. This allows generating instrumental covers from songs to help music students learn instruments without relying on professionals to create covers. The proposed approach could make a variety of instrumental covers more widely available and assist those learning music.
IRJET- Implementation of Emotion based Music Recommendation System using SVM ...IRJET Journal
This document describes a proposed emotion-based music recommendation system that uses facial expression recognition and an SVM algorithm. The system aims to suggest songs to users based on their detected emotion state in order to save them time in manually selecting songs. It would use computer vision components like OpenCV to determine a user's emotion from facial expressions. Once an emotion is recognized, the SVM model would suggest a song matching that emotion. The system aims to automate mood-based playlist creation and improve the music enjoyment experience. It outlines the methodology, including using OpenCV for facial recognition, an SVM algorithm to classify emotions detected, natural language processing for chatbot responses, and IFTTT for response recording.
IRJET- Music Genre Recognition using Convolution Neural NetworkIRJET Journal
1. The document describes a study that uses a Convolutional Neural Network (CNN) model to classify music genres based on labeled Mel spectrograms of audio clips.
2. A CNN model is trained on a dataset of 1000 audio clips across 10 genres. The trained model is then used to classify new, unlabeled audio clips by genre based on their Mel spectrogram representation.
3. CNNs are well-suited for this task as their convolutional layers can extract hierarchical features from the Mel spectrogram images that are indicative of different genres. The study aims to develop an automated music genre classification system using deep learning techniques.
Automatic Music Generation Using Deep LearningIRJET Journal
This document discusses automatic music generation using deep learning. It begins with an abstract describing how music is generated in the form of a sequence of ABC notes using deep learning concepts. LSTM or GRUs are commonly used for music generation as recurrent neural networks that can efficiently model sequences. The main purpose of the project described is to generate melodious and rhythmic music automatically using a recurrent neural network. It reviews approaches like WaveNet and LSTM for music generation and tools like Magenta and DeepJazz. The design uses a character RNN and LSTM network to classify and predict the next character in an ABC notation sequence to generate music.
Audio Signal Identification and Search Approach for Minimizing the Search Tim...aciijournal
Audio or music fingerprints can be utilize to implement an economical music identification system on a
million-song library, however the system needs great deal of memory to carry the fingerprints and indexes.
Therefore, for a large-scale audio library, memory imposes a restriction on the speed of music
identifications. So, we propose an efficient music identification system which used a kind of space-saving
audio fingerprints. For saving space, original finger representations are sub-sample and only one quarters
of the original data is reserved. In this approach, memory demand is far reduced and therefore the search
speed is criticalincreasing whereas the lustiness and dependability ar well preserved. Mapping
audio information to time and frequency domain for the classification, retrieval or identification tasks
presents four principal challenges. The dimension of the input should be considerably reduced;
the ensuing options should be strong to possible distortions of the input; the feature should be informative
for the task at hand simple. We propose distortion free system which fulfils all four of these requirements.
Extensive study has been done to compare our system with the already existing ones, and the results show
that our system requires less memory, provides fast results and achieves comparable accuracy for a largescale database.
KEYWORDS
AUDIO SIGNAL IDENTIFICATION AND SEARCH APPROACH FOR MINIMIZING THE SEARCH TIM...aciijournal
This document describes an approach for improving the speed of audio fingerprint searches in large audio databases. It proposes using a more compact representation of audio fingerprints that reduces the memory requirements, while still maintaining accuracy. The key steps are: 1) extracting fingerprints from audio clips by transforming them into spectrograms and filtering specific frequency bands, 2) further compressing the fingerprints using wavelet decomposition and selecting the most informative components, and 3) indexing the compressed fingerprints using min-hash to allow fast retrieval of similar fingerprints from the database. The approach aims to significantly reduce search time compared to existing audio fingerprinting systems, while achieving comparable accuracy.
Audio Signal Identification and Search Approach for Minimizing the Search Tim...aciijournal
Audio or music fingerprints can be utilize to implement an economical music identification system on a
million-song library, however the system needs great deal of memory to carry the fingerprints and indexes.
Therefore, for a large-scale audio library, memory imposes a restriction on the speed of music
identifications. So, we propose an efficient music identification system which used a kind of space-saving
audio fingerprints. For saving space, original finger representations are sub-sample and only one quarters
of the original data is reserved. In this approach, memory demand is far reduced and therefore the search
speed is criticalincreasing whereas the lustiness and dependability ar well preserved. Mapping
audio information to time and frequency domain for the classification, retrieval or identification tasks
presents four principal challenges. The dimension of the input should be considerably reduced;
the ensuing options should be strong to possible distortions of the input; the feature should be informative
for the task at hand simple. We propose distortion free system which fulfils all four of these requirements.
Extensive study has been done to compare our system with the already existing ones, and the results show
that our system requires less memory, provides fast results and achieves comparable accuracy for a largescale database.
The document discusses developing a model to compose monophonic world music using deep learning techniques. It proposes using a bi-axial recurrent neural network with one axis representing time and the other representing musical notes. The network will be trained on a dataset of MIDI files describing pitch, timing, and velocity of notes. It will also incorporate information from music theory on scales, chords, and other elements extracted from sheet music files. The goal is to generate unique musical sequences while adhering to music theory rules. The model aims to address the problem of composing long durations of background music for public spaces in an automated way.
This document discusses a method for extracting vocals from songs and converting them to instrumental covers using deep learning techniques. It involves using the Spleeter library to separate vocals from music tracks. The extracted vocals can then be converted to instrumental covers for different instruments using a DDSP (Differentiable Digital Signal Processing) library combined with pretrained convolutional neural networks. This allows generating instrumental covers from songs to help music students learn instruments without relying on professionals to create covers. The proposed approach could make a variety of instrumental covers more widely available and assist those learning music.
IRJET- Implementation of Emotion based Music Recommendation System using SVM ...IRJET Journal
This document describes a proposed emotion-based music recommendation system that uses facial expression recognition and an SVM algorithm. The system aims to suggest songs to users based on their detected emotion state in order to save them time in manually selecting songs. It would use computer vision components like OpenCV to determine a user's emotion from facial expressions. Once an emotion is recognized, the SVM model would suggest a song matching that emotion. The system aims to automate mood-based playlist creation and improve the music enjoyment experience. It outlines the methodology, including using OpenCV for facial recognition, an SVM algorithm to classify emotions detected, natural language processing for chatbot responses, and IFTTT for response recording.
IRJET- Music Genre Recognition using Convolution Neural NetworkIRJET Journal
1. The document describes a study that uses a Convolutional Neural Network (CNN) model to classify music genres based on labeled Mel spectrograms of audio clips.
2. A CNN model is trained on a dataset of 1000 audio clips across 10 genres. The trained model is then used to classify new, unlabeled audio clips by genre based on their Mel spectrogram representation.
3. CNNs are well-suited for this task as their convolutional layers can extract hierarchical features from the Mel spectrogram images that are indicative of different genres. The study aims to develop an automated music genre classification system using deep learning techniques.
Automatic Music Generation Using Deep LearningIRJET Journal
This document discusses automatic music generation using deep learning. It begins with an abstract describing how music is generated in the form of a sequence of ABC notes using deep learning concepts. LSTM or GRUs are commonly used for music generation as recurrent neural networks that can efficiently model sequences. The main purpose of the project described is to generate melodious and rhythmic music automatically using a recurrent neural network. It reviews approaches like WaveNet and LSTM for music generation and tools like Magenta and DeepJazz. The design uses a character RNN and LSTM network to classify and predict the next character in an ABC notation sequence to generate music.
Audio Signal Identification and Search Approach for Minimizing the Search Tim...aciijournal
Audio or music fingerprints can be utilize to implement an economical music identification system on a
million-song library, however the system needs great deal of memory to carry the fingerprints and indexes.
Therefore, for a large-scale audio library, memory imposes a restriction on the speed of music
identifications. So, we propose an efficient music identification system which used a kind of space-saving
audio fingerprints. For saving space, original finger representations are sub-sample and only one quarters
of the original data is reserved. In this approach, memory demand is far reduced and therefore the search
speed is criticalincreasing whereas the lustiness and dependability ar well preserved. Mapping
audio information to time and frequency domain for the classification, retrieval or identification tasks
presents four principal challenges. The dimension of the input should be considerably reduced;
the ensuing options should be strong to possible distortions of the input; the feature should be informative
for the task at hand simple. We propose distortion free system which fulfils all four of these requirements.
Extensive study has been done to compare our system with the already existing ones, and the results show
that our system requires less memory, provides fast results and achieves comparable accuracy for a largescale database.
KEYWORDS
AUDIO SIGNAL IDENTIFICATION AND SEARCH APPROACH FOR MINIMIZING THE SEARCH TIM...aciijournal
This document describes an approach for improving the speed of audio fingerprint searches in large audio databases. It proposes using a more compact representation of audio fingerprints that reduces the memory requirements, while still maintaining accuracy. The key steps are: 1) extracting fingerprints from audio clips by transforming them into spectrograms and filtering specific frequency bands, 2) further compressing the fingerprints using wavelet decomposition and selecting the most informative components, and 3) indexing the compressed fingerprints using min-hash to allow fast retrieval of similar fingerprints from the database. The approach aims to significantly reduce search time compared to existing audio fingerprinting systems, while achieving comparable accuracy.
Audio Signal Identification and Search Approach for Minimizing the Search Tim...aciijournal
Audio or music fingerprints can be utilize to implement an economical music identification system on a
million-song library, however the system needs great deal of memory to carry the fingerprints and indexes.
Therefore, for a large-scale audio library, memory imposes a restriction on the speed of music
identifications. So, we propose an efficient music identification system which used a kind of space-saving
audio fingerprints. For saving space, original finger representations are sub-sample and only one quarters
of the original data is reserved. In this approach, memory demand is far reduced and therefore the search
speed is criticalincreasing whereas the lustiness and dependability ar well preserved. Mapping
audio information to time and frequency domain for the classification, retrieval or identification tasks
presents four principal challenges. The dimension of the input should be considerably reduced;
the ensuing options should be strong to possible distortions of the input; the feature should be informative
for the task at hand simple. We propose distortion free system which fulfils all four of these requirements.
Extensive study has been done to compare our system with the already existing ones, and the results show
that our system requires less memory, provides fast results and achieves comparable accuracy for a largescale database.
This document discusses the use of artificial intelligence in organized sound as surveyed in the journal Organised Sound. It provides an overview of key AI technologies like Auto-Tune audio processing that can correct pitch and organize sound. Applications discussed include general sound classification, open sound control for music networking, and time-frequency representations for sound analysis and resynthesis. The document also outlines recent research on intelligent composer assistants, responsive instruments, and recognition of musical sounds. Finally, it discusses the future of AI in organizing sound through planning and machine learning.
Audio Signal Identification and Search Approach for Minimizing the Search Tim...aciijournal
Audio or music fingerprints can be utilize to implement an economical music identification system on a
million-song library, however the system needs great deal of memory to carry the fingerprints and indexes.
Therefore, for a large-scale audio library, memory
imposes a restriction on the speed of music
identifications. So, we propose an efficient music
identification system which used a kind of space-saving
audio fingerprints. For saving space, original finger representations are sub-sample and only one quarters
of the original data is reserved. In this approach,
memory demand is far reduced and therefore the search
speed is criticalincreasing whereas the lustiness and dependability are well preserved. Mapping
audio information to time and frequency domain for
the classification, retrieval or identification tasks
presents four principal challenges. The dimension o
f the input should be considerably reduced;
the ensuing options should be strong to possible distortions of the input; the feature should be informative
for the task at hand simple. We propose distortion
free system which fulfils all four of these requirements.
Extensive study has been done to compare our system
with the already existing ones, and the results sh
ow
that our system requires less memory, provides fast
results and achieves comparable accuracy for a large-
scale database.
AUTOMATED MUSIC MAKING WITH RECURRENT NEURAL NETWORKJennifer Roman
The document describes an automated music making system using recurrent neural networks that allows users to generate music online by specifying genres, types, and length, with the goal of making music generation more accessible; it details the architecture of the system and the recurrent neural network model used to generate MIDI music files based on training from existing music datasets; and several challenges of music generation are discussed along with related work on algorithmic music generation.
This document proposes an end-to-end neural approach for optical music recognition (OMR) of monophonic scores. It trains a neural network model using a dataset of over 80,000 real musical score images paired with their symbolic transcripts. The model combines convolutional and recurrent neural networks to process the image and sequential output. Experimental results demonstrate the neural approach can successfully perform OMR in an end-to-end manner on the monophonic scores. The study provides a starting point for developing scalable neural models for OMR of various printed and handwritten musical scores.
IRJET - Music Generation using Deep LearningIRJET Journal
This document discusses generating music using deep learning techniques like recurrent neural networks (RNNs). It outlines using long short-term memory (LSTM) RNNs to learn patterns from existing music data represented in ABC notation. The model is trained on batches of musical sequences to predict the next character. Once trained, the model can generate new music by probabilistically selecting the next character at each step based on the learned patterns. Further improvements discussed include training on more data from multiple instruments to generate higher quality, more varied music. The results show neural networks have potential for automated music composition by learning musical styles.
A new parallel bat algorithm for musical note recognition IJECEIAES
Music is a universal language that does not require an interpreter, where feelings and sensitivities are united, regardless of the different peoples and languages, The proposed system consists of two main stages: the process of extracting important properties using the linear discrimination analysis (LDA) This step is carried out after the initial treatment process using various procedures to remove musical lines, The second stage describes the recognition process using the bat algorithm, which is one of the metaheuristic algorithms after modifying the bat algorithm to obtain better discriminating results. The proposed system was supported by parallel implementation using the (developed bat algorithm DBA), which increased the speed of implementation significantly. The method was applied to 1250 different images of musical notes. The proposed system was implemented using MATLAB R2016a, Work was done on a Windows10 Processor OS (Intel ® Core TM i5-7200U CPU @ 2.50GHZ 2.70GHZ) computer.
IRJET - EMO-MUSIC(Emotion based Music Player)IRJET Journal
This document describes a proposed emotion-based music player system called EMO-MUSIC. The system uses facial expression recognition via a Haar cascade classifier to identify a user's emotion in real-time. It then generates a playlist of songs matching the detected emotion by accessing pre-defined music directories for each emotion category. This provides a more automated music selection process compared to traditional music players that require manual playlist selection. The system aims to reduce the time users spend browsing for music that suits their mood.
This document provides information about the CS407 Neural Computation course. It outlines the lecturer, timetable, assessment, textbook recommendations, and covers topics from today's lecture including an introduction to neural networks, their inspiration from the brain, a brief history, applications, and an overview of topics to be covered in the course.
MUSZIC GENERATION USING DEEP LEARNING PPT.pptxlife45165
To create a Streamlit application for music generation using deep learning, you need to ensure that all the elements of your Python script are correctly set up and that you handle file paths correctly, especially given the specific paths on your system.
A survey on Enhancements in Speech RecognitionIRJET Journal
This document discusses enhancements in speech recognition and provides an overview of the history and basic model of speech recognition. It summarizes key enhancements researchers have made to improve speech recognition, especially in noisy environments. The basic model of speech recognition involves speech input, preprocessing using techniques like MFCCs, classification models like RNNs and HMMs, and output of a transcript. Researchers are working to develop robust speech recognition that can understand speech in any environment.
Knn a machine learning approach to recognize a musical instrumentIJARIIT
An outline is provided of a proposed system to recognize musical instruments using machine learning techniques. The system first extracts features from audio files using the MIR toolbox in Matlab. It then uses a hybrid feature selection method and vector quantization to identify instruments. Specifically, the key audio descriptors are selected and feature vectors are generated and matched to standard vectors to classify the instrument. The k-nearest neighbors algorithm is used for classification. Preliminary results show the system can accurately recognize instruments based on extracted acoustic features.
This paper proposes a neural network-based text-to-speech synthesis system that can generate audio for different speakers, including those not in the training data. The system uses an encoder-decoder model along with a vocoder to convert text to audio for voice cloning. An auto-tuner is also introduced to alter pitch and tone. The paper shows the system can synthesize and translate text-to-speech across multiple languages using a small amount of training data through deep learning. Evaluation shows the model learns high-quality speaker representations and can generate natural-sounding speech for new voices not seen during training.
This document provides an overview of recent developments in sound recognition techniques. It discusses several methods for sound recognition, including matching pursuit algorithms with MFCC features, probabilistic distance support vector machines using generalized gamma modeling of STE features, and frequency vector principal component analysis. The document also reviews related literature on environmental sound recognition using time-frequency audio features and sound event recognition. It aims to present an updated survey on sound recognition methods and discuss future research trends in the field.
Voice Recognition Based Automation System for Medical Applications and for Ph...IRJET Journal
This document describes a voice recognition-based automation system for medical applications and physically challenged patients. The system uses a voice recognition model, Arduino microcontroller, relays, LEDs, buzzers, and a motor to control an adjustable bed. Voice commands are recognized using techniques like MFCC and HMM and used to control devices via the Arduino. The system is intended to allow paralyzed patients to control devices like lights, alarms, and their bed using only voice commands for increased independence. Testing showed the system provided accurate voice recognition under various conditions.
Voice Recognition Based Automation System for Medical Applications and for Ph...IRJET Journal
This document describes a voice recognition-based automation system for medical applications and physically challenged patients. The system uses a voice recognition model, Arduino microcontroller, relays, LEDs, buzzers, and a motor to control an adjustable bed. Voice commands are recognized using techniques like MFCC and HMM and used to control devices via the Arduino. The system is intended to allow paralyzed patients to control devices like lights, alarms, and their bed using only voice commands for increased independence. Testing showed the system can accurately recognize commands and control devices with 99% accuracy under suitable conditions.
CONTENT BASED AUDIO CLASSIFIER & FEATURE EXTRACTION USING ANN TECNIQUESAM Publications
Audio signals which include speech, music and environmental sounds are important types of media. The problem of distinguishing audio signals into these different audio types is thus becoming increasingly significant. A human listener can easily distinguish between different audio types by just listening to a short segment of an audio signal. However, solving this problem using computers has proven to be very difficult. Nevertheless, many systems with modest accuracy could still be implemented. The experimental results demonstrate the effectiveness of our classification system. The complete system is developed in ANN Techniques with Autonomic Computing system
IRJET- Music Genre Classification using Machine Learning Algorithms: A Compar...IRJET Journal
This document presents research on classifying music genres using machine learning algorithms. The researchers built multiple classification models using the Free Music Archive dataset and compared the models' performance in predicting genre accuracy. Some models were trained on mel-spectrograms of songs and their audio features, while others used only spectrograms. The researchers found that a convolutional neural network model trained solely on spectrograms achieved the highest accuracy among the tested models. The goal of the research was to develop a machine learning approach for automatic music genre classification that performs better than existing methods.
Streaming Audio Using MPEG–7 Audio Spectrum Envelope to Enable Self-similarit...TELKOMNIKA JOURNAL
The ability of traditional packet level Forward Error Correction approaches can limit errors for
small sporadic network losses but when dropouts of large portions occur listening quality becomes an
issue. Services such as audio-on-demand drastically increase the loads on networks therefore new, robust
and highly efficient coding algorithms are necessary. One method overlooked to date, which can work
alongside existing audio compression schemes, is that which takes account of the semantics and natural
repetition of music through meta-data tagging. Similarity detection within polyphonic audio has presented
problematic challenges within the field of Music Information Retrieval. We present a system which works
at the content level thus rendering it applicable in existing streaming services. Using the MPEG–7 Audio
Spectrum Envelope (ASE) gives features for extraction and combined with k-means clustering enables
self-similarity to be performed within polyphonic audio.
The document discusses audio mining, which uses speech recognition technology to analyze digitized audio content like newscasts and meetings and create searchable indexes. It describes two main approaches: text-based indexing that converts speech to text, and phoneme-based indexing that works with sounds instead of text. Several challenges of audio mining are discussed, such as improving precision for applications like medical transcription. Potential uses of audio mining include analyzing customer service calls and intercepted phone conversations.
This document proposes an automatic emotion recognition system that analyzes audio information to classify human emotions. It uses spectral features and MFCC coefficients for feature extraction from voice signals. Then, a deep learning-based LSTM algorithm is used for classification. The system is evaluated on three audio datasets. Recurrent convolutional neural networks are proposed to capture temporal and frequency dependencies in speech spectrograms. The system aims to improve on existing methods which have lower accuracy and require more computational resources for implementation.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
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This document discusses the use of artificial intelligence in organized sound as surveyed in the journal Organised Sound. It provides an overview of key AI technologies like Auto-Tune audio processing that can correct pitch and organize sound. Applications discussed include general sound classification, open sound control for music networking, and time-frequency representations for sound analysis and resynthesis. The document also outlines recent research on intelligent composer assistants, responsive instruments, and recognition of musical sounds. Finally, it discusses the future of AI in organizing sound through planning and machine learning.
Audio Signal Identification and Search Approach for Minimizing the Search Tim...aciijournal
Audio or music fingerprints can be utilize to implement an economical music identification system on a
million-song library, however the system needs great deal of memory to carry the fingerprints and indexes.
Therefore, for a large-scale audio library, memory
imposes a restriction on the speed of music
identifications. So, we propose an efficient music
identification system which used a kind of space-saving
audio fingerprints. For saving space, original finger representations are sub-sample and only one quarters
of the original data is reserved. In this approach,
memory demand is far reduced and therefore the search
speed is criticalincreasing whereas the lustiness and dependability are well preserved. Mapping
audio information to time and frequency domain for
the classification, retrieval or identification tasks
presents four principal challenges. The dimension o
f the input should be considerably reduced;
the ensuing options should be strong to possible distortions of the input; the feature should be informative
for the task at hand simple. We propose distortion
free system which fulfils all four of these requirements.
Extensive study has been done to compare our system
with the already existing ones, and the results sh
ow
that our system requires less memory, provides fast
results and achieves comparable accuracy for a large-
scale database.
AUTOMATED MUSIC MAKING WITH RECURRENT NEURAL NETWORKJennifer Roman
The document describes an automated music making system using recurrent neural networks that allows users to generate music online by specifying genres, types, and length, with the goal of making music generation more accessible; it details the architecture of the system and the recurrent neural network model used to generate MIDI music files based on training from existing music datasets; and several challenges of music generation are discussed along with related work on algorithmic music generation.
This document proposes an end-to-end neural approach for optical music recognition (OMR) of monophonic scores. It trains a neural network model using a dataset of over 80,000 real musical score images paired with their symbolic transcripts. The model combines convolutional and recurrent neural networks to process the image and sequential output. Experimental results demonstrate the neural approach can successfully perform OMR in an end-to-end manner on the monophonic scores. The study provides a starting point for developing scalable neural models for OMR of various printed and handwritten musical scores.
IRJET - Music Generation using Deep LearningIRJET Journal
This document discusses generating music using deep learning techniques like recurrent neural networks (RNNs). It outlines using long short-term memory (LSTM) RNNs to learn patterns from existing music data represented in ABC notation. The model is trained on batches of musical sequences to predict the next character. Once trained, the model can generate new music by probabilistically selecting the next character at each step based on the learned patterns. Further improvements discussed include training on more data from multiple instruments to generate higher quality, more varied music. The results show neural networks have potential for automated music composition by learning musical styles.
A new parallel bat algorithm for musical note recognition IJECEIAES
Music is a universal language that does not require an interpreter, where feelings and sensitivities are united, regardless of the different peoples and languages, The proposed system consists of two main stages: the process of extracting important properties using the linear discrimination analysis (LDA) This step is carried out after the initial treatment process using various procedures to remove musical lines, The second stage describes the recognition process using the bat algorithm, which is one of the metaheuristic algorithms after modifying the bat algorithm to obtain better discriminating results. The proposed system was supported by parallel implementation using the (developed bat algorithm DBA), which increased the speed of implementation significantly. The method was applied to 1250 different images of musical notes. The proposed system was implemented using MATLAB R2016a, Work was done on a Windows10 Processor OS (Intel ® Core TM i5-7200U CPU @ 2.50GHZ 2.70GHZ) computer.
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This document describes a voice recognition-based automation system for medical applications and physically challenged patients. The system uses a voice recognition model, Arduino microcontroller, relays, LEDs, buzzers, and a motor to control an adjustable bed. Voice commands are recognized using techniques like MFCC and HMM and used to control devices via the Arduino. The system is intended to allow paralyzed patients to control devices like lights, alarms, and their bed using only voice commands for increased independence. Testing showed the system can accurately recognize commands and control devices with 99% accuracy under suitable conditions.
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Audio signals which include speech, music and environmental sounds are important types of media. The problem of distinguishing audio signals into these different audio types is thus becoming increasingly significant. A human listener can easily distinguish between different audio types by just listening to a short segment of an audio signal. However, solving this problem using computers has proven to be very difficult. Nevertheless, many systems with modest accuracy could still be implemented. The experimental results demonstrate the effectiveness of our classification system. The complete system is developed in ANN Techniques with Autonomic Computing system
IRJET- Music Genre Classification using Machine Learning Algorithms: A Compar...IRJET Journal
This document presents research on classifying music genres using machine learning algorithms. The researchers built multiple classification models using the Free Music Archive dataset and compared the models' performance in predicting genre accuracy. Some models were trained on mel-spectrograms of songs and their audio features, while others used only spectrograms. The researchers found that a convolutional neural network model trained solely on spectrograms achieved the highest accuracy among the tested models. The goal of the research was to develop a machine learning approach for automatic music genre classification that performs better than existing methods.
Streaming Audio Using MPEG–7 Audio Spectrum Envelope to Enable Self-similarit...TELKOMNIKA JOURNAL
The ability of traditional packet level Forward Error Correction approaches can limit errors for
small sporadic network losses but when dropouts of large portions occur listening quality becomes an
issue. Services such as audio-on-demand drastically increase the loads on networks therefore new, robust
and highly efficient coding algorithms are necessary. One method overlooked to date, which can work
alongside existing audio compression schemes, is that which takes account of the semantics and natural
repetition of music through meta-data tagging. Similarity detection within polyphonic audio has presented
problematic challenges within the field of Music Information Retrieval. We present a system which works
at the content level thus rendering it applicable in existing streaming services. Using the MPEG–7 Audio
Spectrum Envelope (ASE) gives features for extraction and combined with k-means clustering enables
self-similarity to be performed within polyphonic audio.
The document discusses audio mining, which uses speech recognition technology to analyze digitized audio content like newscasts and meetings and create searchable indexes. It describes two main approaches: text-based indexing that converts speech to text, and phoneme-based indexing that works with sounds instead of text. Several challenges of audio mining are discussed, such as improving precision for applications like medical transcription. Potential uses of audio mining include analyzing customer service calls and intercepted phone conversations.
This document proposes an automatic emotion recognition system that analyzes audio information to classify human emotions. It uses spectral features and MFCC coefficients for feature extraction from voice signals. Then, a deep learning-based LSTM algorithm is used for classification. The system is evaluated on three audio datasets. Recurrent convolutional neural networks are proposed to capture temporal and frequency dependencies in speech spectrograms. The system aims to improve on existing methods which have lower accuracy and require more computational resources for implementation.
Similar to Application of Recurrent Neural Networks paired with LSTM - Music Generation (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.