AI-Based Music Mood Composer Using Deep Learning Techniques

Abstract

AI Music Creation mood-based composition using artificial intelligence has becomea novel interdisciplinary field combining signal processing, affective computing, and deep learning. This paper proposes a music-generatingAI based on a user’s emotional context by leveraging a combination of recurrent neural networks (RNNs), transformer architectures, and audio feature embeddings. The system incorporates emotion recognition via audio or text input, followed by real-time music generation aligned with the detected mood (e.g., happy, sad, calm, energetic). A custom The dataset was compiled from open-access sources annotated with emotion labels. The proposed architecture achieves highmood-classification accuracy and generates harmonically rich, emotionally aligned music sequences. This study explores both performance and interpretability using attention heatmaps and feature saliency analysis to enhance transparency and user trust in generative AI systems.

Introduction

Music is a powerful emotional medium. With AI advancements, there's growing interest in generating music that aligns with human emotions. Traditional rule-based systems struggle to capture emotional nuances, but deep learning models—especially RNNs, LSTMs, and Transformers—have significantly improved music generation capabilities.

This study proposes a multimodal AI system that:

• Recognizes human emotion via text and audio inputs

• Generates MIDI-based music aligned with the detected mood

• Evaluates outputs using objective metrics and human feedback

2. Related Work

A. Symbolic Music Generation

• Early methods: Rule-based, Markov models — lacked flexibility.

• Deep learning shift: Google’s Magenta, OpenAI’s MuseNet used LSTM and Transformer models for better compositions.

B. Mood & Emotion Recognition

• Audio Features: MFCC, Chroma, Spectral Contrast

• Text Features: BERT-based sentiment analysis

• Enables personalized, emotionally relevant content generation.

C. Multimodal Emotion Recognition

• Combines audio, text, and facial cues.

• Uses CNN-RNN and Transformer models for richer context and higher accuracy.

D. Emotion-to-Music Mapping

• Datasets like DEAM, EmoMusic link musical elements (tempo, key, mode) to emotion tags.

• Challenges remain due to the subjectivity of emotional interpretation.

E. Transformer Models in Composition

• Models like Music Transformer outperform LSTMs in long-term structure and harmonic consistency.

• Emotion conditioning is still a developing area.

F. Explainability in Music AI

• Tools like attention maps and Grad-CAM help visualize model decisions, increasing user trust and interpretability.

3. Methodology

A. Data Collection & Preprocessing

• Audio: IEMOCAP, RAVDESS datasets for emotion-labeled speech

• Text: LyricsEmotion, EmpatheticDialogues for emotion-labeled conversations

• Music: MIDI files from MAESTRO, DEAM, EmoMusic tagged with 5 emotions: Happy, Sad, Calm, Angry, Energetic

• Features extracted: MFCCs, BERT embeddings, note sequences, key/tempo normalization

B. Emotion Classification

• Audio Classifier: CNN + BiLSTM hybrid model

• Text Classifier: Fine-tuned BERT

• Fusion: Soft voting ensemble for combining both modalities

C. Music Generation

• Model: Emotion-conditioned Transformer-based generator

• Input: MIDI tokens + emotion label embeddings

• Training: Categorical Cross-Entropy loss, Adam optimizer, 5-fold validation

D. Evaluation Metrics

• Objective: BLEU Score, Tonal Coherence, Polyphonic Score

• Subjective: Human ratings on mood accuracy, emotional engagement, and musicality

• Explainability: Attention maps and Grad-CAM highlight decision factors

4. Experiments & Results

A. Emotion Detection Accuracy

B. Music Generation Metrics

C. Human Evaluation (80 Participants)

• Participants favored transformer-generated music for its emotional depth and realism.

5. Comparison with Existing Systems

Conclusion

This study presents a novel framework for generating emotionally aligned music based on multimodal user inputs using deep learning. The system effectively integrates: 1) Emotion recognition via audio and text 2) Music generation using Transformer-based models 3) Explainability tools for model transparency The hybrid model achieved high classification accuracy (94.5%) in emotion detection and outperformed traditional methods in music generation tasks, producing compositions that listeners rated highly for mood accuracy and musicality. Future Work 1) Real-Time Generation and Deployment Optimizing model size and inference speed to support on-device or web-based real-time music generation. 2) Multi-Sensory Emotion Detection Incorporating facial expressions and physiological sensors (e.g., heart rate, EEG) for deeper emotional analysis. 3) Adaptive Personalization Fine-tuning music outputs based on user preferences, mood history, and cultural context. 4) Clinical and Therapeutic Applications Exploring integration into music therapy tools for stress reduction, mental health monitoring, and emotional well-being. 5) Interactive Music Interfaces developing user-facing applications with visual dashboards and emotion sliders for controlling musical parameters interactively.

References

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Copyright

Copyright © 2025 Sagar T, Harshitha M M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.