Wavetable Sampling Synthesis Massie, Dana C.
Applications of Digital Signal Processing to Audio and Acoustics
Book Chapter
Sampling Wavetable Synthesis (“sampling”) is possibly the most commercially popular music synthesis technique in use today (1997). The techniques used in sampling include the traditional playback of ...digitized audio waveforms from RAM wavetables, combined with sample rate conversion to provide pitch shifting. Sampling evolved from traditional computer music wavetable synthesis techniques, where the wavetable size simply grew to include an entire musical note. Extensions to simple wavetable playback include looping of waveforms, enveloping of waveforms, and filtering of waveforms to provide for improved expressivity, i.e., spectral and time structure variation of the perfomed notes. A simple comparison is given between band limited sample rate conversion for pitch shifting, linear interpolation, and traditional computer music phase increment oscillator design.
•A teaching model based on Hidden Markov Model (HMM) algorithm is studied and optimized using genetic algorithm.•The HMM algorithm-based music note recognition model can improve the quality of music ...teaching.•The model has potential for application in the field of music teaching.•The minimum value of the objective function was about 0.739 when the variance probability and crossover probability were 0.02 and 0.6 respectively.
With the development of information technology, computer technology is also gradually applied to the teaching activities of art education, and the use of multimedia technology to assist music teaching has become one of the hot research areas in universities. In order to better cultivate university students' musical exploration ability and creativity, a music note feature recognition teaching model based on Hidden Markov Model (HMM) algorithm is studied and optimized in universities by using genetic algorithm based on HMM algorithm. The music note feature recognition teaching model studied in this article combines computer multimedia technology, signal processing technology, and music theory, and uses computers to simulate the process of human cognition and analysis of music. And in this article, a music note recognition system was constructed using the features of sound level contours combined with the HMM algorithm. The data extracted during music recognition was compressed using the energy compression feature of sound level contours. At the same time, maximum likelihood estimation was used to find the optimal chord sequence, i.e., the optimal path, for the input signal. In the experimental results, the minimum value of the objective function was about 0.739 when the variance probability and crossover probability were 0.02 and 0.6, respectively. In the results, the HMM algorithm-based music note recognition model can improve the quality of music teaching and has some potential for application in the field of music teaching.
The aim of this study is to process the notes on the music sheet, written by hand on the musical note line or edited in the computer environment, with the image processing technique and to play the ...music over the phone application. This application will be developed with Java in the Android Studio environment and image processing will be done using the OpenCV Library. Detection of a note will be memorized by calculating the value and duration of the note. After these sounds, the frequency values of the notes will be available on the phone.
Musical notation is one thing that needs to be learned to play music. This notation has an important role in music because it can help in visualizing instructions for playing musical instruments and ...singing. Unfortunately, musical symbols that are commonly written in musical notation are difficult for beginners who have just started learning music. This research proposed a solution to create an optical music recognition (OMR) using a deep learning model to classify musical notes more accurately with some of the latest convolutional neural network (CNN) architectures. The research was carried out by implementing vision transformer (ViT), CoAtNet-0, and ConvNeXt-Tiny architecture. The training process was also combined with data augmentation to provide more information for the model to learn. Then the accuracy results of each model were compared to find out the best model for the OMR solution in this research. This experiment uses the Andrea dataset and Attwenger dataset which both get the best result by using the augmentation method and ConvNeXt-Tiny as the model. The best accuracy for the Andrea dataset is 98.15% and for the Attwenger dataset is 98.43%.
The security of information passed through the communication channel has become a major concern. Encoding of the messages before transmitting through the channel is most vital. We propose a three ...level encryption (encoding) using musical notes in traditional Indian and Western system as well as the American Standard Code for Information Interchange (ASCII). A unique method for the decryption is also mentioned in the paper.
Human action recognition (HAR) is growing in machine learning with a wide range of applications. One challenging aspect of HAR is recognizing human actions while playing music, further complicated by ...the need to recognize the musical notes being played. This paper proposes a deep learning-based method for simultaneous HAR and musical note recognition in music performances. We conducted experiments on Morin khuur performances, a traditional Mongolian instrument. The proposed method consists of two stages. First, we created a new dataset of Morin khuur performances. We used motion capture systems and depth sensors to collect data that includes hand keypoints, instrument segmentation information, and detailed movement information. We then analyzed RGB images, depth images, and motion data to determine which type of data provides the most valuable features for recognizing actions and notes in music performances. The second stage utilizes a Spatial Temporal Attention Graph Convolutional Network (STA-GCN) to recognize musical notes as continuous gestures. The STA-GCN model is designed to learn the relationships between hand keypoints and instrument segmentation information, which are crucial for accurate recognition. Evaluation on our dataset demonstrates that our model outperforms the traditional ST-GCN model, achieving an accuracy of 81.4%.
Music is a universal language that does not require an interpreter, where feelings and sensitivities are united, regardless of the different peoples and languages, The proposed system consists of two ...main stages: the process of extracting important properties using the linear discrimination analysis (LDA) This step is carried out after the initial treatment process using various procedures to remove musical lines, The second stage describes the recognition process using the bat algorithm, which is one of the metaheuristic algorithms after modifying the bat algorithm to obtain better discriminating results. The proposed system was supported by parallel implementation using the (Developed Bat Algorithm DBA), which increased the speed of implementation significantly. The method was applied to 1250 different images of musical notes. The proposed system was implemented using MATLAB R2016a, Work was done on a Windows10 Processor OS (Intel ® Core TM i5-7200U CPU @ 2.50GHZ 2.70GHZ) computer.
Chunking is defined as information compression by means of encoding meaningful units. To advance the understanding of chunking in musical memory, the present study tested characteristics of melodic ...sequences that might enable a parsimonious memory representation, namely, the presence of a clear tonal context and of melodic cells with clear labels. Musical note symbols, which formed either triads (Experiment 1) or cadences (Experiment 2), were presented visually and sequentially to musically experienced participants for immediate serial recall. The melodic sequences were varied on the within-participant factors list length (long vs. short list) and tonal structure (chunking-supportive vs. chunking-obstructive). Chunking-supportive sequences contained tones from a single diatonic key that formed melodic cells with a clear label, such as “C major triad”. Transitional errors showed that participants grouped notes into melodic cells. Mixed logistic regression modeling revealed that recall was more accurate in chunking-supportive sequences and that this advantage was more pronounced for more experienced participants in the long list length condition of Experiment 2. The findings suggest that a clear tonal context and melodic cells with clear labels benefit chunking in melodic processing, but that the subtleties of the process are additionally influenced by type, size, and number of melodic cells.
Vowels are the most musical and sonic elements of speech. Previous studies found non-arbitrary associations between vowel intrinsic pitch and musical pitch in senseless syllables. In songs containing ...strings of senseless syllables, vowels are connected to melodic direction in close correspondence to their
intrinsic pitch
or the frequency of the second formant F2. This paper shows that also
vowel intrinsic duration
is related to musical patterns. It is generally assumed that low vowels like a ɔ o have a higher intrinsic duration than high vowels like i y u and that there is a positive correlation between the first formant F1 and duration. Analyzing 20 traditional Alpine yodels I found that vowels with longer intrinsic duration tend to align with longer notes, whereas vowels with shorter intrinsic duration with shorter notes. This new result might shed some light on size-sound symbolism in general: Since there is a direct match between vowel intrinsic duration and the “size” of musical notes, there is no need to explain the “size” of musical notes via Ohala's “frequency code” hypothesis. Moreover, I will argue that the iconic associations found between vowel acoustics and musical patterns support the idea of a sound-symbolic musical protolanguage. Such a protolanguage may have started with vowel syllables conveying pitch, timbre, as well as emotional, indexical, and sound-symbolic information.
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