4,315 research outputs found
Music Generation by Deep Learning - Challenges and Directions
In addition to traditional tasks such as prediction, classification and
translation, deep learning is receiving growing attention as an approach for
music generation, as witnessed by recent research groups such as Magenta at
Google and CTRL (Creator Technology Research Lab) at Spotify. The motivation is
in using the capacity of deep learning architectures and training techniques to
automatically learn musical styles from arbitrary musical corpora and then to
generate samples from the estimated distribution. However, a direct application
of deep learning to generate content rapidly reaches limits as the generated
content tends to mimic the training set without exhibiting true creativity.
Moreover, deep learning architectures do not offer direct ways for controlling
generation (e.g., imposing some tonality or other arbitrary constraints).
Furthermore, deep learning architectures alone are autistic automata which
generate music autonomously without human user interaction, far from the
objective of interactively assisting musicians to compose and refine music.
Issues such as: control, structure, creativity and interactivity are the focus
of our analysis. In this paper, we select some limitations of a direct
application of deep learning to music generation, analyze why the issues are
not fulfilled and how to address them by possible approaches. Various examples
of recent systems are cited as examples of promising directions.Comment: 17 pages. arXiv admin note: substantial text overlap with
arXiv:1709.01620. Accepted for publication in Special Issue on Deep learning
for music and audio, Neural Computing & Applications, Springer Nature, 201
Prosody in text-to-speech synthesis using fuzzy logic
For over a thousand years, inventors, scientists and researchers have tried to reproduce human speech. Today, the quality of synthesized speech is not equivalent to the quality of real speech. Most research on speech synthesis focuses on improving the quality of the speech produced by Text-to-Speech (TTS) systems. The best TTS systems use unit selection-based concatenation to synthesize speech. However, this method is very timely and the speech database is very large. Diphone concatenated synthesized speech requires less memory, but sounds robotic. This thesis explores the use of fuzzy logic to make diphone concatenated speech sound more natural. A TTS is built using both neural networks and fuzzy logic. Text is converted into phonemes using neural networks. Fuzzy logic is used to control the fundamental frequency for three types of sentences. In conclusion, the fuzzy system produces f0 contours that make the diphone concatenated speech sound more natural
Neural Speech Synthesis with Transformer Network
Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2)
are proposed and achieve state-of-the-art performance, they still suffer from
two problems: 1) low efficiency during training and inference; 2) hard to model
long dependency using current recurrent neural networks (RNNs). Inspired by the
success of Transformer network in neural machine translation (NMT), in this
paper, we introduce and adapt the multi-head attention mechanism to replace the
RNN structures and also the original attention mechanism in Tacotron2. With the
help of multi-head self-attention, the hidden states in the encoder and decoder
are constructed in parallel, which improves the training efficiency. Meanwhile,
any two inputs at different times are connected directly by self-attention
mechanism, which solves the long range dependency problem effectively. Using
phoneme sequences as input, our Transformer TTS network generates mel
spectrograms, followed by a WaveNet vocoder to output the final audio results.
Experiments are conducted to test the efficiency and performance of our new
network. For the efficiency, our Transformer TTS network can speed up the
training about 4.25 times faster compared with Tacotron2. For the performance,
rigorous human tests show that our proposed model achieves state-of-the-art
performance (outperforms Tacotron2 with a gap of 0.048) and is very close to
human quality (4.39 vs 4.44 in MOS)
A Context-based Numeral Reading Technique for Text to Speech Systems
This paper presents a novel technique for context based numeral reading in Indian language text to speech systems. The model uses a set of rules to determine the context of the numeral pronunciation and is being integrated with the waveform concatenation technique to produce speech out of the input text in Indian languages. For this purpose, the three Indian languages Odia, Hindi and Bengali are considered. To analyze the performance of the proposed technique, a set of experiments are performed considering different context of numeral pronunciations and the results are compared with existing syllable-based technique. The results obtained from different experiments shows the effectiveness of the proposed technique in producing intelligible speech out of the entered text utterances compared to the existing technique even with very less storage and execution time
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