Automatic Notes Generation for Musical Instrument

www.ijemr.net ISSN (ONLINE): 2250-0758, ISSN (PRINT): 2394-6962

Volume-5, Issue-5, October-2015 International Journal of Engineering and Management Research Page Number: 326-330

Automatic Notes Generation for Musical Instrument – Tabla

Prashant Kanade1, Bhavesh Chachra2, Harmeet Sawlani3, Vishal Jumnani4, Deepak Bhagtani5 1Assistant Professor, Department of Computer, V.E.S.I.T, INDIA 2,3,4,5Student, Department of Computer, V.E.S.I.T, INDIA

ABSTRACT This fundamentally implies that Tabla compositions work as a In this paper, our purpose is to design an intellectual system language. David Courtney, an expert Tabla player has made an which will generate the notes of the tabla based on the input string analogy about learning the theory of Tabla which supports the provided by the user. The main idea of the proposed system is divided connection between Tabla compositions and linguistics in “Just into two phases the string checking and generation phase and the as knowledge of grammar separates a literate speaker from the string playing and storage phase. This system will accept the strings masses, likewise an understanding of the theory of Indian and check whether the input strings are valid or not and will apply rhythm and compositional form separates a master from one some probability distribution based algorithm on strings and generate who is merely functional.” some combination of strings (tones generation from string) in the string checking and generation phase and the generated combination II. DESCRIPTION of strings (generated tones) which are melodious are played and the tone which is selected is saved in the users database and downloaded Kaida: Kaida are basically rules which are used to specify the in computer. bols present in the tabla. There are different types of kaida.

Tabla: Tabla is an ancient Indian musical instrument; it is used Keywords---- Teen, Rhythm, Western music by classical music artists. The tones of tabla are called Alphabets. There are about 16-17 bols. Each bol represents the sound which is generated by playing tabla. Each stroke played on tabla represents sound which can be vocally pronunced. I. INTRODUCTION Each bol is specific. Combination of these bols is used to play one string. Long and long patterns can be generated using these Indian classical music is one of the most oldest and unbroken bols. While constructing these patterns two things are to be traditions in the world. It’s origin stem from the Vedas, the ancient script considered Raag & Taal. Raag is melody of the pattern of the Hindus. Presently, Indian classical music is based upon two main generated and Taal is the rhythm of the words.The Tabla has elements: Rag and Tal. Rag is the melody of the music while the Tal is pair of drums which has right-hand drum Dayan & left-hand concerned with the rhythm. This project aims to deal with the idea of drum Bayan. Talis nothing but "clap". The clap used in the rhythm in Indian classical music, specifically the control of rhythmic performance is replaced by tal. Tali or bhari, khali, bol, theka, patterns using an instrument called the Tabla. Tal literally means ‘clap’. It avartan, vibhag or (ang), lay, sam & matra. Certain rules are to implies the control of rhythmic patterns in Indian classical music by the be followed while constructing the patterns, some of the rules use of hand claps. Today the Tabla is used in place of the clap in a are Kaida, Teen tal (16 beats). performance but the origin of the word remains. The Tabla consist of 2 drums the Dayan and the Bayan. The instrument is used not to just control For instance teen tal can be shown as: the rhythmic patterns but can also create large, complex compositions based upon rhythmic structures and strict formalisms. One of the most Dha Dhin Dhin Dha | unique attributes of Indian classical music is the oral transmission of music Dha Dhin Dhin Dha | between musicians and listeners due to the lack of formal written notation Na Tin Tin Ta | unlike Western music. As a result Tabla compositions bare a strong tete Dhin Dhin Dha | relationship to linguistics and natural language. It is my intention to explore this idea by trying to construct a program which uses formal grammars taken from the study of linguistics as a way in which to model Tabla compositions.

Architecture of LSTM: III. METHODOLOGY USED The diagram give below is the architectural view of the LSTM. 1. N-gram Modeling It gives full overview about the working and the methodology of the LSTM. N-Gram modeling is an approach of data analysis designed for sequential streams of data. The principle is elementary, quite literally. A stream of data is split into its constituent terms, or n- grams, as they are called, and the information extracted from the relationships between individual n-grams is used to construct an n- gram model, which can then be used for tasks like predicting the next term in a sequence, or constructing new sequences similar to an existing one. The concept is easily demonstrated using a simple example. Let us consider a sequence of letters S = {ABAACAABAA}. Let us construct an n-gram model on this data using a simple histogram. Splitting the stream into its constituent letters, we see that there are 7 As, 2 Bs and 1 C. We can now estimate P (A) = 7/10, i.e. there is a 70% chance that the next element in the sequence will be an A. A more sophisticated approach would be to use bi-grams, or groups of 2 letters. To estimate the probability of the next term in the sequence being an A, we are looking for the probability of the bi-gram AA, since the last term in the sequence S is an A. P(A|A) = P(AA)/(P(AA) +P(AB) +P(AC)) = 3/(3 + 2 + 1) = 50%. This means that the probability of A being the next element in the sequence is 50% and not 70% as we predicted earlier! Histogram showing the list of all possible bi-grams and their counts in a sequence S = {ABAACAABAA}. Bi-gram AA AB AC BA BB BC CA CB CC Counts 3 2 1 2 0 0 1 0 0 A model that uses still more information can be constructed using sequences of tri-grams, or even 4-grams and so on. A typical implementation of an LSTM block.

N-gram modeling has proved itself to be a robust approach to sequential data analysis. It has been applied rather successfully in speech processing and statistical natural language Steps Involved in LSTM Working: processing, and is now being applied to music modeling as well. 1) Learning Chords: Perhaps the most important advantage of using n-gram modeling LSTM is used to learn musical chord structure. is that it has an inherent approach to addressing the hierarchy within a sequence. Chords structure of LSTM: LSTM was trained using a form In music, as with language, a single stream of events of blues common in jazz bebop improvisation. The form is in 12 can often be interpreted in multiple ways - as a stream of notes, bars and contains the following chords. as a set of gestures and short phrases, or as a coherent melody - and considering n-grams of different lengths allows us to consider all options before making a prediction about the next event. 2. LSTM

LSTM stands for Long short-term memory (LSTM). Chords having 12 bars It is a methodology which works similar to that of neural network which is used for generating different types of tones, songs, etc based on the input given by the user.

2) Learning Melody and chords: Initially chord structure must be learnt fully. The network is started with a series of notes and then notes are compose freely.

Chords composed by LSTM

Working of LSTM :-

Structure of Feed Forward Neural Network

4. RECURRENT NEURAL NETWORKS The recurrent neural network is a type of neural network which works on the methodology of single input – single output. Each output produced by the process becomes input to the other process. This is done recurrently to obtain a melodious song.

3. FEED FORWARD NEURAL NETWORKS Feed forward neural networks works on the concept of accepting the few strings from the user and these strings are processed using the probability distribution based algorithm. The new string generated is further processed using the current input and the other inputs by the user the process is fully based on the probability distribution algorithm. Consider an example, if the user inputs Dha Dhin Dha Dhin then the processing is done using the concept of the probability distribution algorithm. The output generated is not dependent on the past strings generated. This generated output is combination of the input strings. But only those values are displayed that satisfy the rules of the feed forward neural networks.

Structure of Recurring Neural Network

The multiple feed forward neural networks are used to V. EXAMPLE generate the output for the complex values. The string generated from above example is Dha Dhin Dhin Dha. Consider an example of certain tabla bols to demonstrate that how This is used in generation of songs, tones, lyrics, etc. the system will generate its corresponding strings.

Dha Dhin Dhin Dha 1 2 3 4 Dha Dhin Dhin Dha 5 6 7 8 Dha Tin Tin Ta 9 10 11 12 Ta Dhin Dhin Dha 13 14 15 16

Teen Taal has 16 Bits and it has 4 sections with 4 beats in each section we can fit a tihai as follows in 16 Beats of Teen-Taal.

DhaDha TeTe DhaDha TinNa | 1 Dha # DhaDha TeTe |

Structure of Neural Networks DhaDha TinNa Dha # | DhaDha TeTe DhaDha TinNa |

IV. SYSTEM BLOCK DIAGRAM 16 Dha ← First Bit of time Cycle of teen Taal

# represents pause

As shown above N-gram modeling is applied to define bi-grams & tri-grams if required to predict the next alphabet which should have the best possible vocab as well as enough rhythmic. This project serves as an assistant for music artists & tabla masters and glitters our Indian culture over western methologies.

V. CONCLUSION

The main intention of this project is to develop a system which uses a derivation of an existing formal grammar(s) and to use it to model the language of Tabla compositions. The system will generate a set of compositions based upon some preliminary parameters and to be able to generate the same compositions in any The above block diagram gives us the in general overview taal. regarding our system and helps us to analyze how different In order to make this a usable system, a front end is to be modules are interred related with each other. The different modules implemented whereby the user could select the parameters and the of our system are Generator, Modifier, Authenticator, Player, etc. composition that is generated would be displayed (as text or audio samples) back to the user. This would serve the purpose of being an intelligent system as well as serving a practical purpose for the listener.

The system which will be designed in this project will provide musical notes to the user. It will generate required mathematical rule base (theka or taal) using artificial intelligence and neural network techniques. The strings which will be generated can be melodious. Artist will be able to play & enhance the generated strings.

VI. ACKNOWLEDGEMENT

We are thankful that our project mentor Mr. Prashant Kanade gave us opportunity to design such a system which recognizes grammar as well as follow the literature rules of tabla instrument and generate its respective combination of strings also called as Taal. As of now, this idea has not been explored yet in terms of musical notes generation specifically for tabla. So our idea will act as sowing the seed of automation in terms of rhythmic notes formation which help typical tabla masters to explore their knowledge. This project serves as an assistant for music artists & tabla masters and glitters our Indian culture over western methologies.

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