---
language:
- id
- en
task_categories:
- audio-classification
tags:
- multimodal
- audio
- math-rock
- midwest-emo
- emotion-recognition
dataset_info:
  features:
    - name: artist
      dtype: string
    - name: song
      dtype: string
    - name: file_name
      dtype: string
    - name: audio
      dtype: audio
    - name: emotion
      dtype: string
  splits:
    - name: train
      num_examples: 4000
configs:
  - config_name: default
    data_files:
      - split: train
        path: "data/train-*"
---

# Neural Math Rock Multimodal Emotion Dataset

## Dataset Description
This corpus is a large-scale multimodal emotion classification dataset specifically developed for Music Information Retrieval (MIR) and emotional computational analysis within complex musical genres, predominantly Math Rock and Midwest Emo. The dataset consists of exactly **4,000 distinct full-length tracks** structured directly from the validated metadata registry.

The primary objective of this corpus is to facilitate robust research in automated audio tagging, computational musicology, and cross-modal emotion prediction utilizing state-of-the-art architectures such as MERT for acoustic feature extraction and text-based transformers for metadata alignment.

---

## Dataset Structure

### Serialization and Ingestion Guidelines
The serialization pipeline is built upon a high-density, memory-insulated sharding architecture consisting of exactly 400 sequential Parquet shards, with each shard containing a fixed block size of 10 tracks. 

To ensure execution stability and eliminate Out-Of-Memory (OOM) anomalies during high-throughput ingestion under CPU boundaries, a deterministic byte-level stream constraint is applied. Audio tracks are systematically truncated to a maximum duration of **180 seconds (3 minutes)** directly at the low-level binary stream interface prior to PyArrow serialization.

For downstream modeling pipelines, utilizing the lazy evaluation streaming protocol is highly recommended to bypass massive localized memory allocations:

```python
from datasets import load_dataset

# Optimal initialization for large-scale binarized streaming
dataset = load_dataset("anggars/neural-mathrock", split="train", streaming=True)