The pursuit of expressive and human-like music generation remains a significant challenge in the field of artificial intelligence (AI). While deep learning has advanced AI music composition and transcription, current models often struggle with long-term structural coherence and emotional nuance. This study presents a comparative analysis of three leading deep learning architectures: Long Short-Term Memory (LSTM) networks, Transformer models, and Generative Adversarial Networks (GANs), for AI-generated music composition and transcription using the MAESTRO dataset. Our key innovation lies in the integration of a dual evaluation framework that combines objective metrics (perplexity, harmonic consistency, and rhythmic entropy) with subjective human evaluations via a Mean Opinion Score (MOS) study involving 50 listeners. The Transformer model achieved the best overall performance (perplexity: 2.87, harmonic consistency: 79.4%, MOS: 4.3), indicating its superior ability to produce musically rich and expressive outputs. However, human compositions remained highest in perceptual quality (MOS: 4.8). Our findings provide a benchmarking foundation for future AI music systems and emphasize the need for emotion-aware modeling, real-time human-AI collaboration, and reinforcement learning to bridge the gap between machine-generated and human-performed music.
Music has developed through technology since its initial recorded notation systems, continuing until contemporary digital audio workstation (DAW) systems. Deep learning has taken a leading role in modernizing the creation process, along with music performance and tone evaluation techniques. Through the power of artificial intelligence (AI), users can obtain generated melodies, piece harmonization, and composer style emulation. Recent advancements enable novel artistic aspects that artists, composers, and researchers can utilize.
The main hurdle in AI music generation involves developing compositions that preserve the harmony of musical structures while accomplishing emotional symbolism and authentic musical expression. To ensure a fair and unbiased comparison between AI-generated and human-composed music, we curated a balanced set of stimuli across all conditions. Each evaluation set consisted of 10 audio samples per category: LSTM, Transformer, GAN, and human-composed music. All stimuli were matched in terms of genre (classical), instrumentation (piano), and approximate duration (30-45 s). This alignment was done to minimize confounding variables such as stylistic or temporal disparities, ensuring that listener ratings reflected model output rather than unrelated acoustic factors. We acknowledge that further refinement in stimulus control -- such as balancing expressive range, dynamics, and musical complexity -- would strengthen future evaluations. As recommended in, we plan to adopt standardized guidelines for perceptual experiment design in subsequent studies to enhance reproducibility and fairness.
Deep learning implements sophisticated approaches for music generation through its recurrent neural networks, LSTM networks, and transformer models. The Musical Instrument Digital Interface (MIDI) and Audio Edited for Synchronous TRacks and Organization (MAESTRO) dataset serves as an essential tool for deep learning models in music generation through its MIDI and Audio Edited for Synchronous TRacks and Organization content. The dataset, composed of high-quality classical piano recordings combined with synchronized MIDI data, enables proper research in transcription work performance modeling and AI composition. To further refine the spectral representation, the Mel spectrogram is computed by applying a Mel filter bank to the power spectrogram. While the Mel scale approximates human auditory pitch perception, it does not capture the full range of perceptual features relevant to musical expressiveness, such as timbral texture, dynamic articulation, or spatial perception. Therefore, although effective for pitch-oriented learning tasks, Mel-scale features have limitations when modeling the broader perceptual experience of music. For richer perceptual modeling, alternative representations such as perceptual linear prediction (PLP) or the constant-Q transform (CQT) may offer improved alignment with human hearing. The dataset provides researchers with the capability to study AI techniques that extract knowledge from human-based performances to create expressive musical compositions with proper structure. A study examines deep learning methods that apply to the MAESTRO dataset for developing and reviewing automated music. A study investigates the capability of neural networks to receive musical composition training, which allows them to generate new pieces with a structured framework and emotional elements. Through deep learning applications, this research enhances the development of AI music generation technology. Modern technology focuses on the combined effects of Artificial Intelligence and musical composition over recent years. During the early phases of computational music creation systems based on predefined musical rules, algorithms were used to produce compositions. This computational method works for specific purposes yet lacks the human quality of emotional expression and adaptability in output. The data-oriented capabilities of deep learning allow it to produce complex musical patterns through automated methods without requiring preprogrammed musical rules. AI models can now interpret and generate music better than ever because both large music datasets and modern computational capabilities are increasing. Neural networks achieve successful results throughout music processing operations such as melody prediction, chord progression modeling and rhythm generation. The modern advances in AI music generation leave out significant human qualities that characterize emotional and nuanced musical compositions.
A research effort exists to establish connections between computer-generated music and music produced by humans. AI can obtain authentic musical performance knowledge from the MAESTRO dataset to teach itself human qualities of musical expressiveness, such as dynamic patterns, alongside rhythmic and phrasing elements. This research conditions AI-produced music to enhance its quality so AI can act as a skilled musical assistant for both composers and musicians. The technology of AI music generation extends its functions past musical composition tasks. This technology proves useful to improve music education while simultaneously aiding transcription processes and delivering live performance accompaniments. The research supports continuous developments of artificial intelligence in artistic realms by developing enhanced deep-learning techniques for musical composition.
The field of music has transformed deep learning because computers now create complex musical compositions and perform analytical and performing operations with enhanced capabilities. Deep learning models extract musical structures from extensive datasets because they do not follow traditional algorithmic rules for generation, but produce more complex compositions as a result. Sequences of data provided by RNNs and LSTM networks allow them to process musical structures, which makes them ideal for chord progressions and musical phrase modeling. OpenAI's MuseNet, together with Google's Music Transformer, represent Transformer-based models that show exceptional performance in producing structured musical sequences. Deep learning models create new musical works in different styles, ranging from classical music and jazz to pop genres. The combination of AIVA with OpenAI's Jukebox enables musicians to produce new musical compositions without much human labor involvement. Through AI technology, programmers can perform human performance evaluation to generate live musical accompaniment. Live performances receive an enhancement through the deep learning adaptation capability that Yamaha's AI-powered piano applies to pace and volume control from performers. The technique of deep learning turns real-time audio into musical notations expressed through MIDI by improving automatic music transcription methods. AI models require the MAESTRO dataset to achieve accurate piano score transcription results. Through deep learning methods streaming platforms including Spotify and Apple Music generate personalized song suggestions for users based on their listening behavior with solutions such as collaborative filtering and convolutional neural networks. The technology can produce musical sequences that match a specific emotional condition of a listener. Using deep learning models from affective computing allows applications in therapy while providing features to gaming and interactive media through the emotional classification of music.
Deep learning is redefining music production by democratizing access to high-quality composition tools. Independent artists can now leverage AI to enhance creativity without extensive musical training. However, the rise of AI-generated music also raises ethical questions about authorship, copyright, and the role of human musicians in an AI-driven industry. As deep learning models evolve, their ability to create, analyze, and personalize music will expand. Future advancements may lead to AI composers collaborating seamlessly with human musicians, further blurring the line between human and machine-generated music.
This research uses the MAESTRO dataset to apply deep learning techniques in music composition and analysis. The primary objective is to develop and evaluate neural network models that generate expressive and structured music compositions. The study aims to bridge the gap between AI-generated and human-composed music by improving musical coherence, emotional depth, and stylistic adaptation.
The key Contributions of this research are:
This study contributes to the growing field of AI-driven music creation, offering technical advancements and new perspectives on the role of deep learning in musical artistry.
This paper is structured as follows: The related work section presents a comprehensive related work on traditional computational approaches and recent deep learning advancements in music generation. The methodology section details the dataset and preprocessing techniques, outlining the structure and feature extraction methods. It also describes the deep learning architectures used, including LSTMs, Transformers, and GANs, along with the training pipeline and evaluation metrics. The results and discussion section covers the experimental setup, hardware specifications, and hyperparameter tuning. The results, comparing AI-generated compositions to human music using objective and subjective assessments. It also highlights the limitations and challenges of AI-based music composition. Finally, the conclusion and future work section concludes the study and outlines future research directions in AI-driven music generation and performance modeling.
