Introduction: Music generation is a complex task that has received significant attention in recent years, and deep learning techniques have shown promising results in this field. Objectives: While extensive work has been carried out on generating Piano and other Western music, there is limited research on generating classical Indian music due to the scarcity of Indian music in machine-encoded formats. In this technical paper, methods for generating classical Indian music, specifically tabla music, is proposed. Initially, this paper explores piano music generation using deep learning architectures. Then the fundamentals are extended to generating tabla music. Methods: Tabla music in waveform (.wav) files are pre-processed using the librosa library in Python. A novel Bi-LSTM with an Attention approach and a transformer model are trained on the extracted features and labels. Results: The models are then used to predict the next sequences of tabla music. A loss of 4.042 and MAE of 1.0814 are achieved with the Bi-LSTM model. With the transformer model, a loss of 55.9278 and MAE of 3.5173 are obtained for tabla music generation. Conclusion: The resulting music embodies a harmonious fusion of novelty and familiarity, pushing the limits of music composition to new horizons.

翻译：引言：音乐生成是一项复杂的任务，近年来受到广泛关注，深度学习技术在此领域展现出令人瞩目的成果。目标：尽管在钢琴及其他西方音乐生成方面已有大量研究，但由于机器可编码格式的印度音乐数据稀缺，针对印度古典音乐生成的探索十分有限。本文提出了一种生成印度古典音乐——特别是塔布拉鼓音乐——的技术方法。首先探索了基于深度学习架构的钢琴音乐生成，随后将基本原理扩展至塔布拉鼓音乐生成。方法：使用Python中的librosa库对波形（.wav）格式的塔布拉鼓音乐进行预处理。基于提取的特征与标签，分别训练了一种带有注意力机制的新型双向LSTM模型与Transformer模型。结果：利用训练好的模型预测塔布拉鼓音乐的后续序列。双向LSTM模型实现了损失值4.042与平均绝对误差1.0814；Transformer模型在塔布拉鼓音乐生成中取得了损失值55.9278与平均绝对误差3.5173。结论：生成的音乐实现了新颖性与熟悉感的和谐融合，将音乐创作的边界推向全新维度。