Kasap Havasi” (Tracks 12 and 13)
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Basics Music Principles E-Book
Basic Music Principles (e-book edition) Copyright © 2011-2013 by Virtual Sheet Music Inc. All rights reserved. No part of this e-book shall be reproduced or included in a derivative work without written permission from the publisher. It can be shared instead anywhere on the web or on printed media in its entirety. No patent liability is assumed with respect to the use of the information contained herein. Although every precaution has been taken in the preparation of this e-book, the publisher and authors assume no responsibility for errors or omissions. Neither is any liability assumed for damages resulting from the use of the information contained herein. REMEMBER! YOU ARE WELCOME TO SHARE AND DISTRIBUTE THIS BOOK ANYWHERE! Trademarks All terms mentioned in this e-book that are known to be trademarks or service marks have been appropriately capitalized. Publisher cannot attest to the accuracy of this information. Use of a term in this e-book should not be regarded as affecting the validity of any trademark or service mark. Virtual Sheet Music® and Classical Sheet Music Downloads® are registered trademarks in USA and other countries. Warning and Disclaimer Every effort has been made to make this e-book as complete and as accurate as possible, but no warranty is implied. The information provided is on an “as is” basis. The authors and the publisher shall have neither liability nor responsibility to any person or entity with respect to any loss or damages arising from the information contained in this e-book. The E-Book’s Website Find out more, contact the author and discuss this e-book at: http://www.virtualsheetmusic.com/books/basicmusicprinciples/ Published by Virtual Sheet Music Inc. -
Dynamic Generation of Musical Notation from Musicxml Input on an Android Tablet
Dynamic Generation of Musical Notation from MusicXML Input on an Android Tablet THESIS Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Laura Lynn Housley Graduate Program in Computer Science and Engineering The Ohio State University 2012 Master's Examination Committee: Rajiv Ramnath, Advisor Jayashree Ramanathan Copyright by Laura Lynn Housley 2012 Abstract For the purpose of increasing accessibility and customizability of sheet music, an application on an Android tablet was designed that generates and displays sheet music from a MusicXML input file. Generating sheet music on a tablet device from a MusicXML file poses many interesting challenges. When a user is allowed to set the size and colors of an image, the image must be redrawn with every change. Instead of zooming in and out on an already existing image, the positions of the various musical symbols must be recalculated to fit the new dimensions. These changes must preserve the relationships between the various musical symbols. Other topics include the laying out and measuring of notes, accidentals, beams, slurs, and staffs. In addition to drawing a large bitmap, an application that effectively presents sheet music must provide a way to scroll this music across a small tablet screen at a specified tempo. A method for using animation on Android is discussed that accomplishes this scrolling requirement. Also a generalized method for writing text-based documents to describe notations similar to musical notation is discussed. This method is based off of the knowledge gained from using MusicXML. -
How to Read Choral Music.Pages
! How to Read Choral Music ! Compiled by Tim Korthuis Sheet music is a road map to help you create beautiful music. Please note that is only there as a guide. Follow the director for cues on dynamics (volume) and phrasing (cues and cuts). !DO NOT RELY ENTIRELY ON YOUR MUSIC!!! Only glance at it for words and notes. This ‘manual’ is a very condensed version, and is here as a reference. It does not include everything to do with reading music, only the basics to help you on your way. There may be !many markings that you wonder about. If you have questions, don’t be afraid to ask. 1. Where is YOUR part? • You need to determine whether you are Soprano or Alto (high or low ladies), or Tenor (hi men/low ladies) or Bass (low men) • Soprano is the highest note, followed by Alto, Tenor, (Baritone) & Bass Soprano NOTE: ! Alto If there is another staff ! Tenor ! ! Bass above the choir bracket, it is Bracket usually for a solo or ! ! ‘descant’ (high soprano). ! Brace !Piano ! ! ! • ! The Treble Clef usually indicates Soprano and Alto parts o If there are three notes in the Treble Clef, ask the director which section will be ‘split’ (eg. 1st and 2nd Soprano). o Music written solely for women will usually have two Treble Clefs. • ! The Bass Clef indicates Tenor, Baritone and Bass parts o If there are three parts in the Bass Clef, the usual configuration is: Top - Tenor, Middle - Baritone, Bottom – Bass, though this too may be ‘split’ (eg. 1st and 2nd Tenor) o Music written solely for men will often have two Bass Clefs, though Treble Clef is used for men as well (written 1 octave higher). -
Sheet Music Unbound
http://researchcommons.waikato.ac.nz/ Research Commons at the University of Waikato Copyright Statement: The digital copy of this thesis is protected by the Copyright Act 1994 (New Zealand). The thesis may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use: Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. Authors control the copyright of their thesis. You will recognise the author’s right to be identified as the author of the thesis, and due acknowledgement will be made to the author where appropriate. You will obtain the author’s permission before publishing any material from the thesis. Sheet Music Unbound A fluid approach to sheet music display and annotation on a multi-touch screen Beverley Alice Laundry This thesis is submitted in partial fulfillment of the requirements for the Degree of Master of Science at the University of Waikato. July 2011 © 2011 Beverley Laundry Abstract In this thesis we present the design and prototype implementation of a Digital Music Stand that focuses on fluid music layout management and free-form digital ink annotation. An analysis of user constraints and available technology lead us to select a 21.5‖ multi-touch monitor as the preferred input and display device. This comfortably displays two A4 pages of music side by side with space for a control panel. The analysis also identified single handed input as a viable choice for musicians. Finger input was chosen to avoid the need for any additional input equipment. -
Automatic Music Transcription Using Sequence to Sequence Learning
Automatic music transcription using sequence to sequence learning Master’s thesis of B.Sc. Maximilian Awiszus At the faculty of Computer Science Institute for Anthropomatics and Robotics Reviewer: Prof. Dr. Alexander Waibel Second reviewer: Prof. Dr. Advisor: M.Sc. Thai-Son Nguyen Duration: 25. Mai 2019 – 25. November 2019 KIT – University of the State of Baden-Wuerttemberg and National Laboratory of the Helmholtz Association www.kit.edu Interactive Systems Labs Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Title: Automatic music transcription using sequence to sequence learning Author: B.Sc. Maximilian Awiszus Maximilian Awiszus Kronenstraße 12 76133 Karlsruhe [email protected] ii Statement of Authorship I hereby declare that this thesis is my own original work which I created without illegitimate help by others, that I have not used any other sources or resources than the ones indicated and that due acknowledgement is given where reference is made to the work of others. Karlsruhe, 15. M¨arz 2017 ............................................ (B.Sc. Maximilian Awiszus) Contents 1 Introduction 3 1.1 Acoustic music . .4 1.2 Musical note and sheet music . .5 1.3 Musical Instrument Digital Interface . .6 1.4 Instruments and inference . .7 1.5 Fourier analysis . .8 1.6 Sequence to sequence learning . 10 1.6.1 LSTM based S2S learning . 11 2 Related work 13 2.1 Music transcription . 13 2.1.1 Non-negative matrix factorization . 13 2.1.2 Neural networks . 14 2.2 Datasets . 18 2.2.1 MusicNet . 18 2.2.2 MAPS . 18 2.3 Natural Language Processing . 19 2.4 Music modelling . -
Concert Orchestra
Concert Orchestra Lesson: April 7, 2020 Learning Target: “I can identify and count basic rhythms” Warm-Up . Goal Warm-Up Review Consider your goal from yesterday. If necessary, change or modify your goal to best meet your needs. Answer the following writing prompts: 1. My goal is important because….. 2. The benefit of achieving this goal is…... Lesson . Downbeat Meter Pulse Rhythm words Time Tempo Signature Do you know the difference? Beat Metronome . Rhythm Words ● Time Signature (4/4, 2/4, ¾, 6/8, 9/8, etc.) ○ How many beats are in a measure, AND ○ What kind of note gets the beat. ● Pulse (“Feeling the beat”) ● Downbeat (The first beat of every measure) ● Tempo (“How fast”) ● Meter (“Strong” and “Weak” beats) . Time Signatures What do they mean? . Time Signatures Top Number = “How many” Beats in a measure Bottom Number = “What Kind” of note gets the beat . Time Signatures = 6 beats = Quarter Note gets the beat . Did you know music is like Math? Time for some word problems 1. If you are in the time signature 4/4, what kind of note gets the beat? 2. If you are in the time signature 5/8 , how many 8th notes are in a measure? 3. If you are in the time signature 3/4 , how many 8th notes are in a measure? . ANSWER KEY 1. If you are in the time signature 4/4, what kind of note gets the beat? The QUARTER NOTE gets the beat. The bottom number 4 means quarter note, an 8 would mean 8th note, 2 would mean half note, etc. -
The Following Is a Glossary of Terms, Some of Which You Won't Readily Find
Communicating with the Band in Their Vocabulary The following is a glossary of terms, some of which you won’t readily find in music textbooks. These are practical, on-the-job, day-to-day terms you will want to know in the real world of performing and singing on stage. These will aid you greatly in communicating with the band in their language. A Capella – Singing alone or unaccompanied, literally in a chapel style, i.e., vocal music without instrumental accompaniment. This can be another good style in the singer’s palette of colors. Accelerando - Means to speed up the music at this point little by little. Ad Lib - Also means rubato and colla voce. This is music that is played out of tempo or out of time where the musicians follow the singer word for word. This also means that in rubato sections of your "Do-it-yourself' charts, you MUST write all the lyrics so that your musicians can follow your words. Otherwise, they haven't the slightest idea where you are at any given point in an ad-lib section. (All music is either in some tempo or it is not.) Agent - One who actually finds you work and books your act. Arrangement - A completely original musical interpretation of a song arranged for one or more pieces of a band. A & R Man - Stands for Artists and Repertoire. This is the person at a record company who listens to your original demos or cassettes for possible consideration of a record deal. Back Beat - Traditionally, beats two and four of a 4/4 repeating pattern. -
Sheet Music Reader Sevy Harris, Prateek Verma Department of Electrical Engineering Stanford University Stanford, CA [email protected], [email protected]
1 Sheet Music Reader Sevy Harris, Prateek Verma Department of Electrical Engineering Stanford University Stanford, CA [email protected], [email protected] Abstract—The goal of this project was to design an image processing algorithm that scans in sheet music and plays the music described on the page. Our algorithm takes a digital image of sheet music and segments it by line. For each line, it detects the note types and locations, and computes the frequency and duration. These frequency and duration pairs are stored in a matrix and fed into the audio synthesizer, which plays back the music. For simple and non-noisy input images, the audio output for this algorithm was recognizable and played the pitches accurately, excluding the presence of accidentals. F 1INTRODUCTION preprocessing functions to enhance the quality Much like the problem of optical character of the image and make it easier to analyze. recognition, there are many useful applications Then, the key objects are detected using mor- for automating the process of reading sheet phological operations. In the final stage, the music. An optical sheet music reader allows detected notes and other objects are combined a musician to manipulate scores with ease. and analyzed to produce frequency and dura- An algorithm that converts an image to note tion pairs that are sent to the synthesizer as a frequencies can easily be transposed to other matrix. keys and octaves. Transposing music is a very tedious process when done by hand. A sheet 2.1 Segmentation and Preprocessing music reader also has the potential to quickly translate handwritten music into a much more First the image is binarized using Otsu’s readable digital copy. -
Attention As a Perspective for Learning Tempo-Invariant Audio Queries
Attention as a Perspective for Learning Tempo-invariant Audio Queries Matthias Dorfer 1 Jan Hajicˇ jr. 2 Gerhard Widmer 1 Abstract Current models for audio–sheet music retrieval via multimodal embedding space learning use con- volutional neural networks with a fixed-size win- dow for the input audio. Depending on the tempo of a query performance, this window captures more or less musical content, while notehead den- sity in the score is largely tempo-independent. In this work we address this disparity with a soft attention mechanism, which allows the model to encode only those parts of an audio excerpt that are most relevant with respect to efficient query Figure 1. Audio–sheet music embedding space learning with soft- codes. Empirical results on classical piano music input-attention on the input audio. indicate that attention is beneficial for retrieval performance, and exhibits intuitively appealing behavior. changes: both to lower and to higher densities of musical events. Our experiments show that attention is indeed a promising way to obtain tempo-invariant embeddings for 1. Introduction cross-modal retrieval. Cross-modal embedding models have demonstrated the abil- ity to retrieve sheet music using an audio query, and vice 2. Audio–Sheet Music Embedding Space versa, based on just the raw audio and visual signal (Dorfer Learning with Attention et al., 2017). A limitation of the system was that the field of view into both modalities had a fixed size. This is most We approach the cross-modal audio-sheet music retrieval pronounced for audio: a human listener can easily recog- problem by learning a low-dimensional multimodal embed- nize the same piece of music played in very different tempi, ding space (32 dimensions) for both snippets of sheet music but when the audio is segmented into spectrogram excerpts and excerpts of music audio. -
All About Music Publishers
ALL ABOUT MUSIC PUBLISHING In the real music business, songwriters write songs - words and music. The songwriters present their song to a music publisher, who then seeks out a record label, one of whose acts might record the songs. If they do, the label pays the publisher a percentage. The publisher pays the writer a percentage. And the writer is happy. The publisher places the song with a record label or performer. They record it and the money starts coming in. Money from record sales goes directly to the publisher, who pays this on to the songwriter. Money from radio and television plays are collected by the royalty organisations who pay the songwriter directly. What do publishers do? Publishers are your promotional vehicle for songs and instrumental themes if you are not retaining and exploiting your own copyrights. In some instances they will nurture and develop singer-songwriter-artistes to help them gain a record deal. They promote and exploit songs and instrumental themes which are signed to them under an assignment of rights publishing contract. Publishers also register the copyrights as assigned to their catalogues with the relevant royalty collection organisations in the countries in which they operate. Music publishers take a percentage of the money/royalties that may be earned and these percentages are set out in the terms and conditions of the publishing contract entered into with the songwriter/composer. Publishers also print sheet music. Publishers collect and distribute money by way of royalties on behalf of and to the writer as received from the various organisations within the music industry, such as the Performing Right Society (PRS) and the Mechanical Copyright Protection Society (MCPS), which are the UK organisations. -
Understanding Optical Music Recognition
1 Understanding Optical Music Recognition Jorge Calvo-Zaragoza*, University of Alicante Jan Hajicˇ Jr.*, Charles University Alexander Pacha*, TU Wien Abstract For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: few introductory materials are available, and furthermore the field has struggled with defining itself and building a shared terminology. In this tutorial, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords. Index Terms Optical Music Recognition, Music Notation, Music Scores I. INTRODUCTION Music notation refers to a group of writing systems with which a wide range of music can be visually encoded so that musicians can later perform it. In this way, it is an essential tool for preserving a musical composition, facilitating permanence of the otherwise ephemeral phenomenon of music. In a broad, intuitive sense, it works in the same way that written text may serve as a precursor for speech. -
Come Together Bass Transcription
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