Generative AI-Powered Framework for Audio Analysis and Conversational Exploration
Bibliographic record
Abstract
This paper introduces a hybrid deep learning system for complex audio interpretation and post time communication utilizing associated hidden Convolutional Neural Networks (CNNs) with transformer based Large Language Models (LLMs) over spectrogram. The system inputs raw audio input in the form of audio signals, and maps them into spectrograms, extracts high level features using CNNs, and asks for fusion of LLM-produced embeddings with it, for adding semantic understanding, and contextual discussions. The multimodal attention technique helps in crossing the audio-linguistic gap and therefore, it is possible that they can have meaningful and context-aware response. The release offers the apps for intelligent assistant, education, intelligent monitoring, and other. Github repository, experimental evaluation presents increase in performance over the state-of-the-art in both experiments, with accuracy at 93.8%, latency at 420 ms and high semantic coherence (BLEU score of 0.74 is obtained). This result proves that the proposed system is usable to offer both user-friendly and intelligent audio exploration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".