A Preliminary Evaluation of Music Visualization
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Literature review and exploration of music visualization as an Information Visualization research area. Presentation examines all forms of visual music, from 2d images and music videos to media player plug-ins and Computer Science publications.
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Introduction to Music Information Retrieval
Music Information Retrieval is about retrieving information from music entities.
The slides will introduce the basic concepts of the music language, passing through different kind of music representations and it will end up describing some low level features that are used when dealing with music entities.
Introduction to Music Information Retrieval
Music Information Retrieval is about retrieving information from music entities.
The slides will introduce the basic concepts of the music language, passing through different kind of music representations and it will end up describing some low level features that are used when dealing with music entities.
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The clothes power point
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Creating MIDI music can be a practical challenge. In the past, working with it was difficult and frustrating
to all but the most accomplished and determined. Now, however, we are offering a powerful Visual Basic
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tempos, speeds, volumes, instruments, rhythms and major scales. This program has a simple, intuitive, and
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Notation (NMN) and immediately create MIDI music. The key feature of this program is the digitalization
of music input. It vastly simplifies creating, editing, and saving MIDI music. MIDI-LAB can be used
virtually anywhere to write music for entertainment, teaching, computer games, and mobile phone
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Introduction to Music Information Retrieval
Music Information Retrieval is about retrieving information from music entities.
The slides will introduce the basic concepts of the music language, passing through different kind of music representations and it will end up describing some low level features that are used when dealing with music entities.
Introduction to Music Information Retrieval
Music Information Retrieval is about retrieving information from music entities.
The slides will introduce the basic concepts of the music language, passing through different kind of music representations and it will end up describing some low level features that are used when dealing with music entities.
Midi Talk 4
1. MIDI (Musical Instrument Digital Interface) was created in 1983 to standardize the exchange of data between electronic musical instruments. It allows electronic musical instruments and computers to communicate and synchronize.
2. MIDI does not produce sound itself, but sends messages about pitch, velocity, and other parameters to MIDI-compatible devices that then generate the sounds.
3. There are many options for MIDI controllers like keyboards, strings, wind and percussion instruments that can be used to input musical data into computers and sequencing software for playback, editing, and recording.
Main problem definition An instrumentation radar collects com.docx
Main problem definition: An instrumentation radar collects complex frequency domain
data from 12 to 18 GHz every 10 MHz. There are two signals of equal amplitude sampled
by the radar. One signal is located at +25 ns and the other signal is located at zero ns.
Each signal can be expressed as 𝑠
=
𝑘𝑒−𝑗2𝜋𝑓𝑡.
Problem 1: [10 points] Create a total signal vector in the frequency domain and plot the
signal as 20*log10(abs(…and on a separate plot show the unwrapped phase. Make sure
that ALL axes have proper values, limits and labels.
Problem 2: [10 points] Compute the time domain of the signal and plot your result as
20*log10(abs(…
Make sure that ALL axes have proper values, limits and labels.
Problem 3: [10 points] Create an FIR filter which can keep only the zero-ns signal. Make
sure that the first filter sidelobe is at least 40 dB down. Plot the filter magnitude and
phase. Plot your result as 20*log10(abs(…
and on a separate plot show the unwrapped
phase. Make sure that ALL axes have proper values, limits and labels.
Problem 4: [20 points] Apply your FIR filter and plot the results in the time domain as
20*log10(abs(…
and on a separate plot show the phase. Make sure that ALL axes have
proper values, limits and labels.
Problem 5: [10 points] Create an IIR filter which can keep only the zero-ns signal. Make
sure that the first filter sidelobe is at least 40 dB down. Plot the filter magnitude and
phase. Plot your result as 20*log10(abs(…
and on a separate plot show the unwrapped
phase. Make sure that ALL axes have proper values, limits and labels.
Problem 6: [20 points] Apply your IIR filter and plot the results in the time domain as
20*log10(abs(…
and on a separate plot show the phase. Make sure that ALL axes have
proper values, limits and labels.
Problem 7: [10 points] On a separate figure plot the results of applying your FIR and IIR
filters on the same axes in the time domain. Use black for the FIR and red for the IIR.
Plot the magnitude as 20*log10(abs(…
and on a separate figure show the phase. Make
sure that ALL axes have proper values, limits and labels.
Problem 8: [10 points] Based on the original statement of the problem, determine the
resolution of the radar system. Then create a new signal with two signals separated by the
resolution and plot the signal in the time domain. Show the magnitude as 20*log10(abs(…
and on a separate figure show the phase. Make sure that ALL axes have proper values,
limits and labels.
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Describe: This is the very basic and factual content o
PopMAG: Pop Music Accompaniment Generation
This paper introduces PopMAG, a model that generates pop music accompaniment across multiple tracks simultaneously. It uses a novel multi-track MIDI representation called MuMIDI that encodes notes from different tracks into a single sequence, allowing the model to explicitly capture dependencies between tracks. The model achieves state-of-the-art results on three datasets based on both subjective listener evaluations and objective metrics. Ablation studies demonstrate the effectiveness of modeling notes as single tokens, using context memory in the encoder/decoder, and including bar/position embeddings.
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Introduction of my research histroy: From instrument recognition to support o...
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Automatic Music Composition with Transformers, Jan 2021
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
Audio-1
Digital audio can be in the form of sampled audio or MIDI data. Sampled audio involves capturing an analog sound wave by taking regular samples of its amplitude at a certain sampling rate. MIDI data instead conveys musical performance instructions rather than the actual sound. While sampled audio requires more storage space, MIDI files are smaller and can be embedded in web pages more easily. When choosing an audio format, considerations include file size, compatibility, and the sound playback capabilities of the end user's system.
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Midi Talk 4
1. MIDI (Musical Instrument Digital Interface) was created in 1983 to standardize the exchange of data between electronic musical instruments. It allows electronic musical instruments and computers to communicate and synchronize.
2. MIDI does not produce sound itself, but sends messages about pitch, velocity, and other parameters to MIDI-compatible devices that then generate the sounds.
3. There are many options for MIDI controllers like keyboards, strings, wind and percussion instruments that can be used to input musical data into computers and sequencing software for playback, editing, and recording.
Main problem definition An instrumentation radar collects com.docx
Main problem definition: An instrumentation radar collects complex frequency domain
data from 12 to 18 GHz every 10 MHz. There are two signals of equal amplitude sampled
by the radar. One signal is located at +25 ns and the other signal is located at zero ns.
Each signal can be expressed as 𝑠
=
𝑘𝑒−𝑗2𝜋𝑓𝑡.
Problem 1: [10 points] Create a total signal vector in the frequency domain and plot the
signal as 20*log10(abs(…and on a separate plot show the unwrapped phase. Make sure
that ALL axes have proper values, limits and labels.
Problem 2: [10 points] Compute the time domain of the signal and plot your result as
20*log10(abs(…
Make sure that ALL axes have proper values, limits and labels.
Problem 3: [10 points] Create an FIR filter which can keep only the zero-ns signal. Make
sure that the first filter sidelobe is at least 40 dB down. Plot the filter magnitude and
phase. Plot your result as 20*log10(abs(…
and on a separate plot show the unwrapped
phase. Make sure that ALL axes have proper values, limits and labels.
Problem 4: [20 points] Apply your FIR filter and plot the results in the time domain as
20*log10(abs(…
and on a separate plot show the phase. Make sure that ALL axes have
proper values, limits and labels.
Problem 5: [10 points] Create an IIR filter which can keep only the zero-ns signal. Make
sure that the first filter sidelobe is at least 40 dB down. Plot the filter magnitude and
phase. Plot your result as 20*log10(abs(…
and on a separate plot show the unwrapped
phase. Make sure that ALL axes have proper values, limits and labels.
Problem 6: [20 points] Apply your IIR filter and plot the results in the time domain as
20*log10(abs(…
and on a separate plot show the phase. Make sure that ALL axes have
proper values, limits and labels.
Problem 7: [10 points] On a separate figure plot the results of applying your FIR and IIR
filters on the same axes in the time domain. Use black for the FIR and red for the IIR.
Plot the magnitude as 20*log10(abs(…
and on a separate figure show the phase. Make
sure that ALL axes have proper values, limits and labels.
Problem 8: [10 points] Based on the original statement of the problem, determine the
resolution of the radar system. Then create a new signal with two signals separated by the
resolution and plot the signal in the time domain. Show the magnitude as 20*log10(abs(…
and on a separate figure show the phase. Make sure that ALL axes have proper values,
limits and labels.
Concert Evaluation Assignment
In a well-written essay of approximately 700 words (typed), create a review of a live professional concert relevant to this course. Discuss the strengths and weaknesses of the concert and focus mainly on the most important/striking elements and qualities of the performance. Use that information to arrive at an overall evaluation of the event.
Hints to keep you writing:
Describe: This is the very basic and factual content o
PopMAG: Pop Music Accompaniment Generation
This paper introduces PopMAG, a model that generates pop music accompaniment across multiple tracks simultaneously. It uses a novel multi-track MIDI representation called MuMIDI that encodes notes from different tracks into a single sequence, allowing the model to explicitly capture dependencies between tracks. The model achieves state-of-the-art results on three datasets based on both subjective listener evaluations and objective metrics. Ablation studies demonstrate the effectiveness of modeling notes as single tokens, using context memory in the encoder/decoder, and including bar/position embeddings.
Two-step Melody Harmonious Generator
This document describes a two-step algorithm for a melody generator program. It begins with an overview of computer music and melody generation. It then outlines the two-step algorithm which first generates rhythmic patterns and then fills those patterns with pitches based on settings like scale and tonic. The document discusses using statistical analysis and rules of harmony to determine harmonic combinations. It introduces the "Harmonicube" pattern-based approach to harmonic melody generation and demonstrates a music generator prototype. It concludes that melody results from rhythm and harmony, rules and limitations are important, and the two-step generator can create harmonious music through patterns.
Technology in music education
The document discusses various ways that technology can be used to support music education. It describes software for music composition, production and notation like GarageBand and Sibelius that allow students to compose and arrange music visually or aurally. It also outlines technologies for performance like MIDI and notation software that facilitate parts and scores. Other sections cover self-paced learning tools for music theory and ear training, using the internet for teaching music history, and strategies for interdisciplinary work.
Web audio, Trackers and Making Music
This document discusses audio on the web and building a tracker in WebAudio. It provides a brief history of audio on the web from <bgsound> to <audio> to Web Audio. It then gives an overview of Web Audio, including precise timing control, pre-buffering sounds, and real-time effects. It describes audio contexts and basic Web Audio examples. It also discusses trackers, their history and use in music creation, notable tracker applications, tracking terminology, and building a tracker in WebAudio. Repositories for related code examples are provided.
Ch8 Section A: Audio Basics
Digital sound works by sampling analog sound waves and representing them with numeric data. For audio CDs, sounds are sampled 44,100 times per second with 16-bit precision. Audio compression reduces file sizes by removing extraneous sounds. Digital audio is played back through sound cards and popular formats include MP3, WAV, and AIFF. MIDI files store musical instrument commands instead of sampled sound. Speech synthesis converts text to speech while recognition converts speech to text.
Introduction of my research histroy: From instrument recognition to support o...
This document introduces Tetsuro Kitahara and summarizes his research history in music information retrieval and automatic music generation. It describes his early work on instrument recognition in polyphonic music using probabilistic models. It then outlines his later research developing probabilistic models for computer-assisted music creation tools that allow users to generate and edit melodies and harmonies through intuitive interfaces. The document emphasizes that his recent works aim to automatically generate music from user inputs while facilitating human-computer interaction through abstract representations that hide implementation details.
Automatic Music Composition with Transformers, Jan 2021
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
Audio-1
Digital audio can be in the form of sampled audio or MIDI data. Sampled audio involves capturing an analog sound wave by taking regular samples of its amplitude at a certain sampling rate. MIDI data instead conveys musical performance instructions rather than the actual sound. While sampled audio requires more storage space, MIDI files are smaller and can be embedded in web pages more easily. When choosing an audio format, considerations include file size, compatibility, and the sound playback capabilities of the end user's system.
Audio
Digital audio can be in the form of sampled audio or MIDI data. Sampled audio involves capturing an analog sound wave through sampling at regular intervals (sample rate) and storing the amplitude measurements digitally. MIDI data instead stores instructions on how to recreate a musical performance without the actual sound recording. While sampled audio provides higher quality sound, MIDI files are smaller in size and can be changed without affecting pitch or quality, making them suitable for early web embedding. Factors like file format, playback capabilities and sound type must be considered when adding audio to multimedia projects.
TECHNOLOGIES OF MUSIC
The document discusses various technologies used for music programming. It begins by explaining how music was traditionally recorded using instruments and various recording devices. It then discusses how technology has advanced the music industry, allowing music to be created solely using computers by understanding how computers process music through recording, editing, filters and synthesizers. The document examines some challenges faced by musicians in learning music programming. It proceeds to summarize several programming languages and tools used for music programming, including Alda for text-based programming, SynthEdit SDK for developing synthesizers, Pure Data for graphical and visual programming, and JFugue Java library.
Notating pop music
What is typical about the way pop music is notated? How do existing file formats and software applications cope with this?
07-02-ACA-Tonal-Music.pdf
This document discusses the representation of pitch in music. It introduces musical pitch intervals and how they are notated, explains how MIDI represents pitch as a numeric value, defines cents as a measure of pitch distance, and discusses temperament, intonation, and vibrato in musical pitch performance and perception.
WM Grade 11- 1st Term Music Technology.pptx
Music technology refers to the use of devices and tools to compose, perform, record, and analyze music. This includes electronic music made using electrical devices to produce and alter sounds, as well as computer music created or assisted by computers. Popular music technologies include MIDI interfaces to synchronize equipment; synthesizers to generate and modify sounds; samplers to record and playback audio; sequencers to record and playback multiple musical tracks; and notation software to write sheet music on computers. These technologies allow musicians to more easily create, edit, and share digital music.
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A Preliminary Evaluation of Music Visualization
- 1. A Preliminary Evaluation of Music Visualization Danny Rado The “clavilux” [above] was invented by Thomas Wilfred in 1924
- 2. ¿ Is Music Visualization an area of research ? Plan Motivation Everyday music visualizations Research The very best of music visualizations Discussion Identify some key issues for the field Determine why the field is underdeveloped
- 6. Itunes Visualization Effects are generated on sound events These visualizations give less information than the standard music video
- 7. Windows Media Player Visualization Frequency bars are the most informative visualizations Still not portraying any depth of music visualization
- 8. Winamp Visualization Stick Figures plug-in allows users to create drawings and animate them to a few frequencies Gives semantic meaning to music information making it easier to identify and follow
- 11. The Important Music Video Hints at the structure of the music Graphics are repeated on musical theme repetition