G. Paavai Anand

Shreeviknesh Sankaran, Sukavanan Nanjundan, G. Paavai Anand

The problem of audio synthesis has been increasingly solved using deep neural networks. With the introduction of Generative Adversarial Networks (GAN), another efficient and adjective path has opened up to solve this problem. In this paper, we present a method to synthesize the singing voice of a person using a Convolutional Long Short-term Memory (ConvLSTM) based GAN optimized using the Wasserstein loss function. Our work is inspired by WGANSing by Chandna et al. Our model inputs consecutive frame-wise linguistic and frequency features, along with singer identity and outputs vocoder features. We train the model on a dataset of 48 English songs sung and spoken by 12 non-professional singers. For inference, sequential blocks are concatenated using an overlap-add procedure. We test the model using the Mel-Cepstral Distance metric and a subjective listening test with 18 participants.

Sukavanan Nanjundan, Shreeviknesh Sankaran, C. R. Arjun et al.

Application of K-Means algorithm is restricted by the fact that the number of clusters should be known beforehand. Previously suggested methods to solve this problem are either ad hoc or require parametric assumptions and complicated calculations. The proposed method aims to solve this conundrum by considering cluster hypersphere density as the factor to determine the number of clusters in the given dataset. The density is calculated by assuming a hypersphere around the cluster centroid for n-different number of clusters. The calculated values are plotted against their corresponding number of clusters and then the optimum number of clusters is obtained after assaying the elbow region of the graph. The method is simple, easy to comprehend, and provides robust and reliable results.