Rhythmic Sequences. Please serious detailed explanations only.
(International Mathematics Olympiad.)
Answers
Answer:
A.G.P is your answer
pls mark as brainliest.
Step-by-step explanation:
source SRP is relevant to a class if there exists a class SRP of the class, whose similarity degree with the source SRP is larger than a similarity degree threshold. For a relevant source SRP, the most similar class SRP is called its target SRP. In the sec- ond stage, we determine how relevant a source SRP is to a class as follows: 1. For each class SRP of a class, its usefulness for classifying music data into each class is first estimated. 2. For each source SRP, a similarity measure is used to identify the corresponding target SRP in each class. 3. By combining the above information, to what degree a source SRP is relevant to each class can be computed. Except for the relevance to each class, the importance of a source SRP with respect to the music object to be classified is also computed. In this way, each source SRP is associated with two kinds of information. We combine them to estimate how possible a music piece belongs to a class. As a result, the music piece will be assigned to the class with the highest score. The experiment results indicate that our approach outper- forms the previous work. The remaining of this paper is organized as follows. Section 2 describes the details of feature extraction. After that, the approach of SRP-based classification is presented in Section 3. Section 4 shows the experiment results with a discussion. Finally, this paper is concluded in Section 5. Given a collection of MIDI files, we first select a representative track for each music piece manually. After that, the feature values of melody and rhythm are extracted from the representative tracks by using a MIDI parser. As a result, we represent each music piece by two symbolic sequences as follows. Rhythm stands for a sequence of beats in music and often brings people various kinds of perception. For example, a rhythm with fast tempos may make some people nervous but others excited. According to the duration of a note, we classify each note into one of the nine types in rhythm, where each type is notated as a distinct symbol called beat symbol . Table 1 shows the set of beat symbols we use in this paper. Except for symbol I, the range of each beat symbol covers exactly a quarter of a beat. In this way, the rhythm of a music piece can be represented by a sequence of beat symbols, called the rhythmic sequence . As shown in Figure 2, the rhythmic sequence of an example is notated as “BBDBBDBBBBBBD”. Melody is a sequence of pitches in music. A music piece with certain styles often contains specific melodies because the composer is used to showing a style by using similar melodies. A pitch interval stands for the difference between the pitch values of two consecutive notes. It is straightforward to transform a melody into a sequence of pitch intervals. According to the length of a pitch interval, we classify each pitch interval into one of the thirteen types in melody, where each type is notated as a distinct symbol called pitch symbol . Table 2 shows the set of pitch symbols we use in this paper. Each type of pitch intervals has two orientations, i.e. from low to high and the inverse. Therefore, we provide a plus or minus sign for each pitch symbol to indicate the orientation. In the set of pitch symbols, we distinguish the major intervals from the minor ones because they often bring people different kinds of perception, e.g. happiness and sadness. In this way, the melody of a music piece can be represented by a sequence of pitch symbols, called the melodic sequence . As shown in Figure 3, the melodic sequence of an example is notated as “+B+D-b-B+C-B+A+b-b- C”. Based on the above representations, a repeating pattern means a consecutive sequence that appears frequently in the rhythmic or melodic sequence of a music piece. Hsu, Liu, and Chen [9] propose an algorithm for finding repeating patterns from a music piece. In this paper, we adapt this algorithm to the needs of music classification by considering the following constraints: Maximum length : Long sequences tend to contain duplicate information. Therefore, the maximum constraint on the sequence length will reduce duplicate information and the extra costs for pattern discovery. Minimum length : Short sequences often have little information about the music and therefore its classification. The minimum constraint on the sequence length will alle- viate the unnecessary loads due to a large amount of short sequences.
option a is the correct one
hope it helps you