Artificial Intelligence Lyrics Generator
Artificial Intelligence lyric generators use text datasets, on this text datasets semantic analysis is done and critical values are calculated to generate lyrics.
This article discusses artificial intelligence algorithms and their use to generate lyrics.
Introduction
To generate lyrics, a text generation algorithm of artificial intelligence is used. Text sequences are processed to generate a single word at each sequential step. To generate words templates are used to generate phrases, this is done based on predefined rules. The most commonly used rule includes the n-gram method.
Advance techniques to generate text work on feed-forward neural networks based on recurrent neural networks. The usage frequency of Recurrent Neural networks is highest among other algorithms and tools of Artificial Intelligence.
The recurrent neural network uses gating functions namely LSTM (Long-Short-Term-Memory) in association with GRU (Gated-Recurrent-Unit) to generate language. Recurrent Neural Network users often suffer from contextual information loss. To remove this insufficiency Recurrent Neural Networks are improved with topics associated with the document, contextual worlds, caching, etc,. To generate the required text recurrent neural network use sequencing of sentences, text structuring, and generating dialogues based on context.
To generate the required text titles of the documents are considered and processed using context-based information. To generate desired phrases labels and tags are used. Using this technique it is possible to predict words to be used as desired.
Generating text to achieve the desired objective is a complex task. To generate the desired text semantic analysis of attributes is done. This semantic analysis is done to generate text and combine them to find meaningful sentences. The sentences are formed using different attributes, labels, and tags.
There exist different lyric generators, SAM (Semantic Attribute Modulation) is one of them. SAM is used to model language and generate style-based text. SAM use categorized attributes that are commonly used.
Semantic Attribute Modulation
An artificial Intelligence-based lyric generator is landscape in the domain of music creation. Artificial intelligence is used to generate music. Artificial Intelligence is creating a new era in the domain of music generation. One artificial intelligence-based music generation company is Audoir.
Audoir uses Semantic Attribute Modulation (SAM), an artificial intelligence-based technique to generate lyrics. Semantic Attribute Modulation uses complex artificial intelligence algorithms. These algorithms are used to generate music.
Music generation has become easy with the use of artificial intelligence algorithms. To generate music different factors need to be considered. These factors include – finding relatable music and finding a smooth structure of the lyrics.
The artificial intelligence algorithm used by semantic attribute modulation is trained on the previous database of songs. The algorithm used by semantic attribute modulation processes the structure and used patterns in the previous successful song database.
The artificial intelligence algorithm used by Semantic Attribute Modulation (SAM) can rank the song produced by the algorithm. The lyrics produced by semantic attribute modulation are checked for plagiarism.
The lyrics are generated using the language model. The language model works on probability. This probability is used by recurrent neural networks. The gating function of the recurrent neural network is used to generate one word at a time. The algorithm uses vectors and a matrix to generate music.
A recurrent neural network is not efficient enough to generate contextual information. Already existing song database is used to generate lyrics, the lyrics are generated based on context. The technique known as bag-of-words, hidden topics, and embedded neural network is used to generate lyrics.
The attributes that are considered to generate lyrics include titles, authors, tags, and associated sentiments. Using these attributes semantic analysis is done to generate lyrics. To generate lyrics appropriate language generation model and lyric style is used to generate music.
Attributes to be Considered
Different semantic attributes are considered such as title attributes and category attributes. The title attributes are decided by the author and act as an abstract of the associated document. A recurrent Neural Network is used to process the hidden state.
The title words are used in association with context embedding. The title attributes are used in association with the attention mechanism. The weighted sum is used to obtain the different titles for different types of text words.
Category attributes are used to process words that are used in reading and writing. Different category attributes are considered these attributes include lyric authorship and analysis of sentiments. The category attributes are used to generate vectors.
Based on the above-stated process semantic attribute extraction is done. Based on this extraction embeddings are obtained. The algorithm uses a function to do a comparative analysis of how the attributes influence the main text.
Recurrent Neural Network framework process semantic attributes. To generate a new word the recurrent neural network reads the semantic attributes. The new word is generated with the help of the gated function. The gated function is part of the gated recurrent unit.
Conclusion
Neural Network uses encoder-decoder to process texts and generate embeddings. The decoder processes these embeddings and generates appropriate words. The Neural Network is also used to generate lyrics after processing keywords. The work is done by the recurrent neural networks to generate lyrics after processing attributes.
The Recurrent Neural Network processes semantic embeddings to generate desired text as compared to the encoder-decoder framework. To generate the desired text context-based language modeling is done. The title and keywords are used with the techniques such as bag-of-words to generate the desired text.
Other attributes that are used to generate lyrics include review rates and categories of documents. The review rate and categories of document attributes are used to generate texts. The lyric is also generated using the encoder-decoder and generator-discrimnator model.
To generate the lyrics semantic analysis of category attributes is done. The semantic analysis is done to interpret the SAM. The attributes are processed using the attention mechanism.
The lyric generator must be tested against the defined datasets. The dataset must be reflected with different attributes. The dataset must be such that it must be able to evaluate the language generation model. The dataset must have training dataset tokens having a high vocabulary size.
An example of a lyrics generator includes XLyrics. The XLyrics is a China-based lyric generator that generates lyrics using the dataset crawled from the internet. This lyrics generator known as XLyrics consists of more than 4 thousand lyrics and works in association with tokens, labels, and tags.
The discussed lyric generator in this article is semantic attribute modulation, this lyric generator is used to lyric using a language model and associated style. The lyric is generated using available interpretable texts. This lyrics generator can generate music using titles, authors, and associated categories.
The attributes used to generate lyrics make the discussed system more powerful. This makes the discussed system more flexible. More attributes must be searched to generate lyrics that are more enjoyable and acceptable.
As per the internet survey, there exists many lyrics generator. These generators are evaluated and rated on different parameters. These parameters are decided based on the artificial intelligence algorithms used and based on the datasets used to train the artificial intelligence algorithms. Based on the internet survey the discussed lyric generator is considered to be a good choice to generate lyrics.
