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The Musician's Guide To Theory And Analysis 4th Edition
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Received: 31 August 2021 / Revised: 24 October 2021 / Accepted: 25 October 2021 / Published: 28 October 2021
The Musician’s Guide To Theory And Analysis (the Musician’s Guide Series): Clendinning, Jane Piper, Marvin, Elizabeth West: 9780393930818: Amazon.com: Books
Recently, the field of collective music creation has gained momentum. In this context, our goal is two-fold: to develop an intelligent and predictive listening model for chord sequences, offering a suitable evaluation of the related musical information retrieval (MIR) task. which represents the real-time extraction of musical chord symbols from a live audio stream and to predict the probability of the duration of the extracted symbolic sequence. Indeed, this application case invites us to question the evaluation process and methodology applied to the substance-based MIR model. In this article, we focus on chords because the average feature is often used to describe harmonic progression in Western music. In the case of strings, there are strong geographic and functional connections. However, most of the research in the field of MIR focuses mainly on the implementation of statistical models based on the chords, without considering the evaluation based on music or learning. In fact, the standard rating is based on the binary degree of the score output (correct chord prediction vs wrong chord prediction). Therefore, we present a chord analyzer specially designed to measure the performance of chord-based models in terms of functional qualification of classification results (by considering the harmonic function of chords). Then, to incorporate musical knowledge into the learning process of the automatic chord extraction task, we propose a specific musical distance to compare the predicted and observed chords. Finally, we investigate the impact of adding high-quality metadata on learning to predict chord sequences (such as critical or pessimistic position information). We have shown that the model can achieve better performance in terms of accuracy or confusion, but with biased results. At the same time, models with lower resolution may produce errors with greater musical significance. Therefore, a goal-oriented evaluation method allows for a better understanding of the results and a more appropriate design of the MIR model.
The concept of musical composition can be defined as the arrangement and relationship between musical elements over time. In addition, a piece of music has different levels of structure depending on the level of time considered. In fact, elements of music such as notes (defined by pitch, duration and timbre) can be grouped into groups such as chords, chords and phrases. Similarly, they can be incorporated into larger structures such as chord progressions or choruses and verses. Therefore, there can be complex, multiscale, hierarchical and temporal relationships between different types of musical elements.
Among these different levels of musical definition, the chord is one of the most extensive features of Western music such as pop or jazz. Chord structure, at a high level, determines the musical intent of the song’s progression. Indeed, in the development of music, it is common for musicians to agree on a set of presets in order to develop a real musical expression with this high-quality system. Therefore, a piece of music can be defined by a sequence of chords which is often called a harmonic structure. Real-time music enhancement systems, such as DYCI2 [1], produce music by combining environmental interactions and expectations about musical descriptions that may take the form of chord sequences in a situation we imagine. However, it would be a real improvement if the system could predict actively the continuation of the sequence taken during the music playing. Therefore, in this paper we will focus on extracting and predicting the order of musical chords from a given musical signal. However, this import situation invites us to question the evaluation process and methodology currently applied in the music information retrieval (MIR) model based on chords. In fact, here we need a model that reaches the level of understanding of basic harmony. Therefore, it seems that the classification scores used for the first time to evaluate the substance-based MIR models are insufficient, and the use of appropriate evaluation methods seems to be essential.
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In this paper, the goal of our application is to combine chord extraction and chord prediction models in an intelligent listening system that predicts chord sequences over time. short (the architecture is detailed in Section 2). The musical impulse, discussed in Section 3, comes from the field of musical improvisation between humans and machines. Here we have chosen to work with chord nomenclature because chords and chord progressions are high-level abstractions that summarize the original signs without exact definition, but with a high understanding of the intention of a musician. Therefore, our application case invites us to ask questions about the methodology and evaluation process applicable to MIR chord-based models. In this line of reasoning, we discuss in Section 4 the differences in properties and functions of chord parameters, as well as the specificity of the evaluation process when used in modeling machine learning (ML) chord labels. To achieve the goal of the application, we divided the project into two main tasks: the listening module and the signal generation module. The Listening Module, presented in Section 5, captures the composition of the chords that the musician performs. Next, the generative model (detailed in Section 6) predicts the sequence of music based on the extracted features. In order to provide consistent satisfaction with the rating products, the evaluation process is implemented according to the models based on the two ingredients.
Although improvisation is about spontaneity, it is based on rules and structures that allow different musicians to play well together. In blues or jazz improvisation, these structures often rely on chord progressions that set the direction for the entire performance, and their conclusions become essential. Indeed, in group improvisation it is necessary to understand the basic musical structure. Therefore, our application case is to develop a system that communicates with musicians in real time by summarizing the progression of the expected chords.
Figure 1 shows the general workflow of a predictive and intelligent listening system. In this architecture, the input is a waveform obtained by recording music in real time. First, the music signal is processed to obtain a temporal representation of the frequency, from which the chord sequence is extracted. Thus, there is a chord assignment for each pulse of the input signal. Finally, the prediction model takes this chord sequence and suggests possible next steps. We also add feedback to use the prediction to improve local string extraction (more information on the relationship between two string-dependent models will be presented in Section 7).
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Our motivation is in the field of human music improvisation with machines, where the system of improvisation is created by listening to human musicians and provides specific conditions for artificial music operators. In fact, the first independent creative use of the Intelligent Listening and Prediction model was to use a musician’s strumming as input, with interactive output providing a sequence of chords that could as a guide for other musicians.
In the field of computer science applied to music, co-improvisation comes from the interaction between humans and computer workers. That is, a digital system that can play music according to the musician in real time. This leads to a process of co-creation, where humans listen to the output of the system in real time, themselves modal to the music wave (see Figure 2).
A great example of this concept is Omax [2], which learns the specific characteristics of a musician’s style in real time, and then plays with it from that learned model. Its creative strategy (the second step in Figure 2) is based on the Oracle operator automatons [3], which allows the production of stylistically appropriate music using online or offline data. the internet.
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