slides presented at a three-hour local AI music course in Taiwan in Oct 2021; part 1: a brief introduction to music information retrieval (+analysis, +generation)
20190625 Research at Taiwan AI Labs: Music and Speech AIYi-Hsuan Yang
A very brief introduction of what we have been working on at the AI Labs on "music AI" (specifically, automatic music composition/generation) and "speech AI" (specifically, Mandarin ASR).
Machine learning for creative AI applications in music (2018 nov)Yi-Hsuan Yang
An up-to-date overview of our recent research on music/audio and AI. It contains four parts:
* AI Listener: source separation (ICMLA'18a) and sound event detection (IJCAI'18)
* AI DJ: music thumbnailing (TISMIR'18) and music sequencing (AAAI'18a)
* AI Composer: melody generation (ISMIR'17), lead sheet generation (ICMLA'18b), multitrack pianoroll generation (AAAI'18b), and instrumentation generation (arxiv)
* AI Performer: CNN-based score-to-audio generation (AAAI'19)
Yi-Hsuan Yang is an Associate Research Fellow with Academia Sinica. He received his Ph.D. degree in Communication Engineering from National Taiwan University in 2010, and became an Assistant Research Fellow in Academia Sinica in 2011. He is also an Adjunct Associate Professor with the National Tsing Hua University, Taiwan. His research interests include music information retrieval, machine learning and affective computing. Dr. Yang was a recipient of the 2011 IEEE Signal Processing Society (SPS) Young Author Best Paper Award, the 2012 ACM Multimedia Grand Challenge First Prize, and the 2014 Ta-You Wu Memorial Research Award of the Ministry of Science and Technology, Taiwan. He is an author of the book Music Emotion Recognition (CRC Press 2011) and a tutorial speaker on music affect recognition in the International Society for Music Information Retrieval Conference (ISMIR 2012). In 2014, he served as a Technical Program Co-chair of ISMIR, and a Guest Editor of the IEEE Transactions on Affective Computing and the ACM Transactions on Intelligent Systems and Technology.
slides presented at a three-hour local AI music course in Taiwan in Oct 2021; part 1: a brief introduction to music information retrieval (+analysis, +generation)
20190625 Research at Taiwan AI Labs: Music and Speech AIYi-Hsuan Yang
A very brief introduction of what we have been working on at the AI Labs on "music AI" (specifically, automatic music composition/generation) and "speech AI" (specifically, Mandarin ASR).
Machine learning for creative AI applications in music (2018 nov)Yi-Hsuan Yang
An up-to-date overview of our recent research on music/audio and AI. It contains four parts:
* AI Listener: source separation (ICMLA'18a) and sound event detection (IJCAI'18)
* AI DJ: music thumbnailing (TISMIR'18) and music sequencing (AAAI'18a)
* AI Composer: melody generation (ISMIR'17), lead sheet generation (ICMLA'18b), multitrack pianoroll generation (AAAI'18b), and instrumentation generation (arxiv)
* AI Performer: CNN-based score-to-audio generation (AAAI'19)
Yi-Hsuan Yang is an Associate Research Fellow with Academia Sinica. He received his Ph.D. degree in Communication Engineering from National Taiwan University in 2010, and became an Assistant Research Fellow in Academia Sinica in 2011. He is also an Adjunct Associate Professor with the National Tsing Hua University, Taiwan. His research interests include music information retrieval, machine learning and affective computing. Dr. Yang was a recipient of the 2011 IEEE Signal Processing Society (SPS) Young Author Best Paper Award, the 2012 ACM Multimedia Grand Challenge First Prize, and the 2014 Ta-You Wu Memorial Research Award of the Ministry of Science and Technology, Taiwan. He is an author of the book Music Emotion Recognition (CRC Press 2011) and a tutorial speaker on music affect recognition in the International Society for Music Information Retrieval Conference (ISMIR 2012). In 2014, he served as a Technical Program Co-chair of ISMIR, and a Guest Editor of the IEEE Transactions on Affective Computing and the ACM Transactions on Intelligent Systems and Technology.
a set of slides introducing the application of machine learning to music related applications; intended for audience not with computer science background;
Research on Automatic Music Composition at the Taiwan AI Labs, April 2020Yi-Hsuan Yang
Slides introducing our ongoing projects on automatic music composition at the Yating Music AI Team of the Taiwan AI Labs (https://ailabs.tw/). The following URLs link to some demo audio files we have put on SoundCloud: all of them were fully automatically generated without any manual post-processing or editing.
@ai_piano demo: https://soundcloud.com/yating_ai/sets/ai-piano-generation-demo-202004
@ai_piano+drum demo: https://soundcloud.com/yating_ai/sets/ai-pianodrum-generation-demo-202004
@ai_guitar demo: https://soundcloud.com/yating_ai/ai-guitar-tab-generation-202003/s-KHozfW0PTv5
Machine Learning for Creative AI Applications in Music (2018 May)Yi-Hsuan Yang
Machine Learning for Creative AI Applications in Music, slides presented at the Fifth Taiwanese Music and Audio Computing Workshop (http://mac.citi.sinica.edu.tw/tmac18/)
Deep Learning with Audio Signals: Prepare, Process, Design, ExpectKeunwoo Choi
Is deep learning Alchemy? No! But it heavily relies on tips and tricks, a set of common wisdom that probably works for similar problems. In this talk, I’ll introduce what the audio/music research societies have discovered while playing with deep learning when it comes to audio classification and regression -- how to prepare the audio data and preprocess them, how to design the networks (or choose which one to steal from), and what we can expect as a result.
Learning to Generate Jazz & Pop Piano Music from Audio via MIR TechniquesYi-Hsuan Yang
This set of slides briefly describes what we have been working on at the Yating Music AI team at the Taiwan AI Labs. We are going to talk about these as two demo papers at the 20th annual conference of the International Society for Music Information Retrieval (ISMIR),
Research in artificial intelligence (AI) is known to have impacted medical diagnosis, stock trading, robot control, and several other fields. Perhaps less popular is the contribution of AI in the field of music. Nevertheless, Artificial intelligence and music (AIM) has, for a long time, been a common subject in several conferences and workshops, including the International Computer Music Conference, the Computing Society Conference and the International Joint Conference on Artificial Intelligence.
Research at MAC Lab, Academia Sincia, in 2017Yi-Hsuan Yang
Some research projects we did in 2017 at the Music & Audio Computing (MAC) Lab, Research Center for IT Innovation, Academia Sinica, Taipei, Taiwan. It includes three parts: 1) vocal separation, 2) music generation, 3) AI DJ.
Social Tags and Music Information Retrieval (Part I)Paul Lamere
Part 1 of the Social Tags and Music Information Retrieval Tutorial. Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear
Automatic Music Composition with Transformers, Jan 2021Yi-Hsuan Yang
An up-to-date version of slides introducing our ongoing projects on automatic music composition at the Yating Music AI Team of the Taiwan AI Labs (https://ailabs.tw/), focusing on introducing the following two publications from our group.
[1] "Pop Music Transformer: Beat-based modeling and generation of expressive Pop piano compositions," in Proc. ACM Multimedia, 2020.
[2] "Compound Word Transformer: Learning to compose full-song music over dynamic directed hypergraphs," in Proc. AAAI 2021.
For the last version of the slides, please visit: https://www2.slideshare.net/affige/research-on-automatic-music-composition-at-the-taiwan-ai-labs-april-2020/edit?src=slideview
ISMIR 2019 tutorial: Generating music with generative adverairal networks (GANs)Yi-Hsuan Yang
Slides Hao-Wen Dong and I presented at the ISMIR 2019 tutorial on "Generating Music with GANs—An Overview and Case Studies". More info: https://salu133445.github.io/ismir2019tutorial/
Public version of the slideshow I used during my presentation about adaptive music in video games and other interactive media at #UXMonday event, organized by http://asociaceux.cz in Prague, March 2, 2015.
Annotating Music Collections: How Content-Based Similarity Helps to Propagate...Oscar Celma
In this paper we present a way to annotate music collections by exploiting audio similarity. In this sense, similarity is used to propose labels (tags) to yet unlabeled songs, based on the content–based distance between them. The main goal of our work is to ease the process of annotating huge music collections, by using content-based similarity distances as a way to propagate labels among songs.
We present two different experiments. The first one propagates labels that are related with the style of the piece, whereas the second experiment deals with mood labels. On the one hand, our approach shows that using a music collection annotated at 40% with styles, and using content– based, the collection can be automatically annotated up to 78% (that is, 40% already annotated and the rest, 38%, only using propagation), with a recall greater than 0.4. On the other hand, for a smaller music collection annotated at 30% with moods, the collection can be automatically annotated up to 65% (e.g. 30% plus 35% using propagation).
Sound Events and Emotions: Investigating the Relation of Rhythmic Characteri...Andreas Floros
A variety of recent researches in Audio Emotion Recognition (AER) outlines high performance and retrieval accuracy results. However, in most works music is considered as the original sound content that conveys the identified emotions. One of the music characteristics that is found to represent a fundamental means for conveying emotions are the rhythm- related acoustic cues. Although music is an important aspect of everyday life, there are numerous non-linguistic and non- musical sounds surrounding humans, generally defined as sound events (SEs). Despite this enormous impact of SEs to humans, a scarcity of investigations regarding AER from SEs is observed. There are only a few recent investigations concerned with SEs and AER, presenting a semantic connection between the former and the listener’s triggered emotion. In this work we analytically investigate the connection of rhythm-related characteristics of a wide range of common SEs with the arousal of the listener using sound events with semantic content. To this aim, several feature evaluation and classification tasks are conducted using different ranking and classification algorithms. High accuracy results are obtained, demonstrating a significant relation of SEs rhythmic characteristics to the elicited arousal.
a set of slides introducing the application of machine learning to music related applications; intended for audience not with computer science background;
Research on Automatic Music Composition at the Taiwan AI Labs, April 2020Yi-Hsuan Yang
Slides introducing our ongoing projects on automatic music composition at the Yating Music AI Team of the Taiwan AI Labs (https://ailabs.tw/). The following URLs link to some demo audio files we have put on SoundCloud: all of them were fully automatically generated without any manual post-processing or editing.
@ai_piano demo: https://soundcloud.com/yating_ai/sets/ai-piano-generation-demo-202004
@ai_piano+drum demo: https://soundcloud.com/yating_ai/sets/ai-pianodrum-generation-demo-202004
@ai_guitar demo: https://soundcloud.com/yating_ai/ai-guitar-tab-generation-202003/s-KHozfW0PTv5
Machine Learning for Creative AI Applications in Music (2018 May)Yi-Hsuan Yang
Machine Learning for Creative AI Applications in Music, slides presented at the Fifth Taiwanese Music and Audio Computing Workshop (http://mac.citi.sinica.edu.tw/tmac18/)
Deep Learning with Audio Signals: Prepare, Process, Design, ExpectKeunwoo Choi
Is deep learning Alchemy? No! But it heavily relies on tips and tricks, a set of common wisdom that probably works for similar problems. In this talk, I’ll introduce what the audio/music research societies have discovered while playing with deep learning when it comes to audio classification and regression -- how to prepare the audio data and preprocess them, how to design the networks (or choose which one to steal from), and what we can expect as a result.
Learning to Generate Jazz & Pop Piano Music from Audio via MIR TechniquesYi-Hsuan Yang
This set of slides briefly describes what we have been working on at the Yating Music AI team at the Taiwan AI Labs. We are going to talk about these as two demo papers at the 20th annual conference of the International Society for Music Information Retrieval (ISMIR),
Research in artificial intelligence (AI) is known to have impacted medical diagnosis, stock trading, robot control, and several other fields. Perhaps less popular is the contribution of AI in the field of music. Nevertheless, Artificial intelligence and music (AIM) has, for a long time, been a common subject in several conferences and workshops, including the International Computer Music Conference, the Computing Society Conference and the International Joint Conference on Artificial Intelligence.
Research at MAC Lab, Academia Sincia, in 2017Yi-Hsuan Yang
Some research projects we did in 2017 at the Music & Audio Computing (MAC) Lab, Research Center for IT Innovation, Academia Sinica, Taipei, Taiwan. It includes three parts: 1) vocal separation, 2) music generation, 3) AI DJ.
Social Tags and Music Information Retrieval (Part I)Paul Lamere
Part 1 of the Social Tags and Music Information Retrieval Tutorial. Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear
Automatic Music Composition with Transformers, Jan 2021Yi-Hsuan Yang
An up-to-date version of slides introducing our ongoing projects on automatic music composition at the Yating Music AI Team of the Taiwan AI Labs (https://ailabs.tw/), focusing on introducing the following two publications from our group.
[1] "Pop Music Transformer: Beat-based modeling and generation of expressive Pop piano compositions," in Proc. ACM Multimedia, 2020.
[2] "Compound Word Transformer: Learning to compose full-song music over dynamic directed hypergraphs," in Proc. AAAI 2021.
For the last version of the slides, please visit: https://www2.slideshare.net/affige/research-on-automatic-music-composition-at-the-taiwan-ai-labs-april-2020/edit?src=slideview
ISMIR 2019 tutorial: Generating music with generative adverairal networks (GANs)Yi-Hsuan Yang
Slides Hao-Wen Dong and I presented at the ISMIR 2019 tutorial on "Generating Music with GANs—An Overview and Case Studies". More info: https://salu133445.github.io/ismir2019tutorial/
Public version of the slideshow I used during my presentation about adaptive music in video games and other interactive media at #UXMonday event, organized by http://asociaceux.cz in Prague, March 2, 2015.
Annotating Music Collections: How Content-Based Similarity Helps to Propagate...Oscar Celma
In this paper we present a way to annotate music collections by exploiting audio similarity. In this sense, similarity is used to propose labels (tags) to yet unlabeled songs, based on the content–based distance between them. The main goal of our work is to ease the process of annotating huge music collections, by using content-based similarity distances as a way to propagate labels among songs.
We present two different experiments. The first one propagates labels that are related with the style of the piece, whereas the second experiment deals with mood labels. On the one hand, our approach shows that using a music collection annotated at 40% with styles, and using content– based, the collection can be automatically annotated up to 78% (that is, 40% already annotated and the rest, 38%, only using propagation), with a recall greater than 0.4. On the other hand, for a smaller music collection annotated at 30% with moods, the collection can be automatically annotated up to 65% (e.g. 30% plus 35% using propagation).
Sound Events and Emotions: Investigating the Relation of Rhythmic Characteri...Andreas Floros
A variety of recent researches in Audio Emotion Recognition (AER) outlines high performance and retrieval accuracy results. However, in most works music is considered as the original sound content that conveys the identified emotions. One of the music characteristics that is found to represent a fundamental means for conveying emotions are the rhythm- related acoustic cues. Although music is an important aspect of everyday life, there are numerous non-linguistic and non- musical sounds surrounding humans, generally defined as sound events (SEs). Despite this enormous impact of SEs to humans, a scarcity of investigations regarding AER from SEs is observed. There are only a few recent investigations concerned with SEs and AER, presenting a semantic connection between the former and the listener’s triggered emotion. In this work we analytically investigate the connection of rhythm-related characteristics of a wide range of common SEs with the arousal of the listener using sound events with semantic content. To this aim, several feature evaluation and classification tasks are conducted using different ranking and classification algorithms. High accuracy results are obtained, demonstrating a significant relation of SEs rhythmic characteristics to the elicited arousal.
2015-05-09 키스텝에서 진행한 딥러닝 개요입니다.
짧은 분량이지만 세미나는 매우 인터랙티브하게 진행되어 두시간을 꽉 채웠던 슬라이드입니다.
다시 말해 슬라이드만 보시면 부족한 부분이 많이 있으니 참고하시기 바랍니다.
8페이지에 6개의 텐서플로 플레이그라운드 데모를 연결해두었습니다. 링크 눌러보시고 직접 돌려보시면 뉴럴넷에 대해 쉽게 이해하실 수 있을겁니다.
Deep Learning for Speech Recognition - Vikrant Singh TomarWithTheBest
Tomar discusses the components of speech recognition, the difference between deep learning for speech and images, system architecture, GMM-HMM based systems, deep neural networks in speech, tandem DNN, and hybrids. There's a lot of exciting stuff to talk about in deep learning communities.
Vikrant Singh Tomar, Founder, Fluent.ai
MusicMood - Machine Learning in Automatic Music Mood Prediction Based on Song...Sebastian Raschka
In this talk, I present a machine learning approach to build a music recommendation system that can identify happy songs in a music library based on song lyrics. Also, this presentation covers a general introduction to predictive modeling, naive Bayes classification, and text classification.
Incorporating data from the Spotify Web API, I expanded on the research for the June 2018 RTG presentation to study submitted memories as links between emotions, activities, and musical qualities using python instead of R.
Understanding ai music discovery and recommendation systemsValerio Velardo
Thanks to Spotify, you’ve got millions of songs at your fingertips, ready to be discovered. And because of its AI-powered recommendation system, you’re more likely to find new music you’re already interested in. Learning what’s behind this technology can assist music business leaders and entrepreneurs to provide a more meaningful, tailored discovery experience for their users. Not only will you gain insight that most music leaders don’t yet have, but also become aware of how to apply this to your business.
In this one-hour webinar, you’ll:
- Learn how music recommendation systems work
- Understand how to leverage these systems to add value to
specific music business applications
- Learn how listeners discover music according to our research
findings
UPDATED VERSION (2011): http://www.slideshare.net/plamere/music-recommendation-and-discovery
As the world of online music grows, music 2.0 recommendation systems become an increasingly important way for music listeners to discover new music.
Commercial recommenders such as Last.fm and Pandora have enjoyed commercial and critical success. But how well do these systems really work? How good are the recommendations? How far into The Long Tail do these recommenders reach?
In this tutorial we look at the current stateof theart in music recommendation. We examine current commercial and research systems, focusing on the advantages and the disadvantages of the various recommendation strategies. We look at some of the challenges in building music recommenders and we explore some of the ways that MIR techniques can be used to improve future recommenders.
Abstract:
This presentation will consider the most effective ways to incorporate music into the EFL classroom and transform your lessons into chart topping hits. It will feature several playlists highlighting the power of music to assist language learning.
Using the presenter’s own experience, it will explore the potential of using music to stimulate and encourage learners to produce meaningful communication. Furthermore, the ideas and activities presented will show you how to integrate music as a rich and essential resource for the EFL classroom.
Finally, the presenter will express how music can improve and strengthen your lessons. By skillfully implementing music into lesson planning it can create a harmonious atmosphere - effectively bringing colour, meaning and rhythm to any class. Therefore, making the learning experience more memorable for you and your students.
Anat Gilboa's thesis presentation to the University of Virginia School of Engineering and Applied Science.
Over the course of her final semester, Anat and Dr. Qi, PhD, tested various methods to determine similarity between songs using features extracted from metadata in the Million Song Dataset.
Metric Learning for Music Discovery with Source and Target PlaylistsYing-Shu Kuo
Playlist generation for music exploration by defining sets of source songs and target songs and deriving a playlist through metric learning and boundary constraints.
https://github.com/hank5925/mlmdstp
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
1. Understanding Music Playlists
10 July 2015
ICML 2015 Workshop - Machine Learning and Music Discovery
Keunwoo Choi
George Fazekas
Mark Sandler
@c4dm @Queen Mary University of London
3. Playlist and Recommendation
• Music recommendation == playlist generation
in many cases; especially for common music listener.
• Because recommending a song doesn’t make sense.
• Because simply picking top-N songs might fail.
4. Music Playlist
• What is playlist?
• “Sequence of music items”
for ( ), by ( ), …
• Ill-posed definition,
inductively defined
by how people use
• Many people use it
• 1.5B playlists on Spotify
5. Different Assumptions
• What is a good playlist?
Sequence of similar songs
Smooth transitions
Fixed start/end song
Given duration
?
6. Datasets
Deezer-2015
144,726 songs
50,000 playlists
during 2007-2015
EchoNest track
features (high-level
features such as
speechness,
dancability, …)
AoTM-2011
97,411 songs
86,310 playlists
during 1998-2011
EchoNest Timbre
Features, energy/
key/loudness/
mode…
+ playlist category
7. Datasets
• Hierarchy of playlist categories
Genre
Rock Jazz Hiphop R&B Electronic Folk
Rock/
Pop
Mixed
Genre
Blues Raggae Country
Punk Hardcore
Dance/
House
Activity
Sleep
Road
trip
Emotion
Break
up
Depression
Others
Indie
Alternating
DJ
Theme
Single
artist
Cover Narrative
13. • No structural difference
• Playlist itself doesn’t represent the user that much.
(Or is not easily observed.)
• Usage data may be required
• Usage hours, number of songs/artists, diversity of
preferences, price tier, social activities, …
14. F2. Similarity vs. Diversity
“Songs in a playlist should be similar”
“Songs in a playlist shouldn’t be too similar”
Similar
Familiar
Unified
vs.
Interesting
Not boring
Diverse
15. • Audio feature similarity between songs
• within-playlist
vs.
arbitrary pairs
17. 3. Different similarity given category
• Compute the similarity of songs in the playlists for each
category (for each feature)
• Get rankings of categories (for each feature)
• Get average of the rankings
• Plot it (with nice colours)
19. Summary
• User (behaviour) information is required to build user
model based on clustering
• Should find an appropriate range of similarity for better
playlist generation
• which varies given dataset, features, and similarity
measure.
• Desired song similarity may be different for each
category
• Different parameters/prior should be set.
20. Future work
, or just curious about…
• How much do people care about playlist?
How much do people put efforts on it?
• Mix Tape/CD was important for us (music researchers),
so as (modern) playlist for people?
• Looks like they are just containers for a set of songs
rather than a sequence songs.