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README.md

@@ -42,7 +42,7 @@ Multimodal LLMs have made substantial progress in perceiving the physical world,
 
 MHB comprises **6,128 instances across 13 classification tasks and seven domains: Astronomy, Earth Science, Healthcare, Electromagnetic, Underwater Acoustics, Bioacoustics, and Manufacturing**. It spans highly heterogeneous input regimes, with sampling rates from approximately 10⁻⁵ to 10⁶ Hz, sequence lengths from 10¹ to 10⁶ samples, and both single- and multi-channel inputs. Each instance consists of a signal, a task-specific textual instruction, and a predefined label set.
 
-## Dataset Licenses and Distribution
+# Dataset Licenses and Distribution
 
 MHB integrates data from multiple sources. The following table lists the source or processed-release licenses and whether the MHB input signals are included in this repository.
 
@@ -64,7 +64,7 @@ MHB integrates data from multiple sources. The following table lists the source
 
 **License notes.** MHB's original benchmark contributions, to the extent the maintainers hold the relevant rights, are released under **CC BY-NC-SA 4.0**. Original data sources and processed releases retain their own applicable conditions and attribution requirements. No waveform files from Iquique, Yaseen, ShipsEar, or DeepShip are redistributed here; their metadata and reconstruction scripts are provided instead.
 
-## Dataset Structure
+# Dataset Structure
 
 The repository contains a unified `meta_data.jsonl` file, task-specific data folders, and processing scripts. Each task folder follows the naming convention `Domain-Task-Task_Name`, with input signals stored under `raw_input/`.
 
@@ -97,7 +97,7 @@ The repository contains a unified `meta_data.jsonl` file, task-specific data fol
 
 The four externally hosted datasets can be reconstructed locally using the corresponding mapping scripts. The `wav_input/` directories and `wav_data.jsonl` are generated on demand by `process/process_raw2wav.py`.
 
-## Tasks
+# Tasks
 
 All 13 tasks in MHB are formulated as classification tasks, with detailed task definitions provided in [Appendix A.1 of the paper](https://arxiv.org/abs/2611.10693).
 
@@ -118,7 +118,7 @@ All 13 tasks in MHB are formulated as classification tasks, with detailed task d
 | Manufacturing | PU_cur | 320 | Bearing fault diagnosis from motor-current signals | `Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis` |
 | **Total** | **13 tasks** | **6,128** | | |
 
-## `meta_data.jsonl` Format
+# `meta_data.jsonl` Format
 
 Each line is a JSON object representing one instance. The following is a shortened example; the text fields are illustrative:
 
@@ -172,7 +172,7 @@ For models that generate additional explanations or text, users may append an ou
 "Output ONLY the classification result. Do not output any other text, explanation, or punctuation."
 ```
 
-## Reconstructing Externally Hosted Datasets
+# Reconstructing Externally Hosted Datasets
 
 The waveforms of **Iquique, Yaseen, ShipsEar, and DeepShip** are not bundled in this release. Download the source data from the linked official providers, then run the relevant script from the **MHB repository root**. Each script uses `meta_data.jsonl` to select the exact MHB subset, creates the required task folder and `raw_input/` directory, and preserves the relative paths already defined in the metadata. No changes to `meta_data.jsonl` are needed.
 
@@ -182,9 +182,7 @@ For the MHB mapping scripts, install the necessary Python packages:
 pip install numpy h5py soundfile
 ```
 
-The upstream split tools, when used, may have their own dependencies.
-
-### Iquique (502 instances)
+## Iquique (502 instances)
 
 **Source:** [SeisBench — Iquique](https://seisbench.readthedocs.io/en/main/pages/data/benchmark_datasets.html#iquique).
 
@@ -196,7 +194,7 @@ python process/map_iquique.py /path/to/Iquique
 
 The script uses `meta_data.trace_name` to extract the specified three-component seismic traces from the HDF5 file and saves 502 NPY files with the names and paths defined in `meta_data.jsonl`.
 
-### Yaseen (200 instances)
+## Yaseen (200 instances)
 
 **Source:** [Official Yaseen GitHub repository](https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-).
 
@@ -214,7 +212,7 @@ python process/map_yaseen.py /path/to/Classification-of-Heart-Sound-Signal-Using
 
 The script selects and copies the 200 specified WAV files from the five categories.
 
-### ShipsEar (84 instances)
+## ShipsEar (84 instances)
 
 **Original data:** [ShipsEar official website](https://underwaternoise.atlanttic.uvigo.es/) (access to the complete dataset may require contacting its authors).
 
@@ -234,9 +232,9 @@ Then run:
 python process/map_shipsear.py /path/to/shipsear_with_split-12class/test
 ```
 
-Following the segmentation protocol associated with the referenced **12-class split**, the mapping script extracts **30-second windows at 15-second intervals**, including the designated Trawler test excerpt, and writes the 84 MHB test WAV files to their specified `raw_input/` paths.
+Following the segmentation protocol associated with the referenced **12-class split**, the mapping script extracts **30-second windows at 15-second intervals**, including the designated Trawler test excerpt, and writes the 84 MHB WAV files to their specified `raw_input/` paths.
 
-### DeepShip (527 instances)
+## DeepShip (527 instances)
 
 **Original data:** [Official DeepShip GitHub](https://github.com/irfankamboh/DeepShip).
 
@@ -263,7 +261,7 @@ python process/map_deepship.py /path/to/DeepShip_test
 
 The script selects the 527 designated three-second test segments, renames each output WAV using the sample `id`, and copies the files to the corresponding `raw_input/` location.
 
-## RAW-to-WAV Conversion
+# RAW-to-WAV Conversion
 
 For Audio LLM APIs that only accept WAV files, MHB provides `process/process_raw2wav.py`. **First reconstruct Iquique**, because its NPY inputs are not included in the repository. From the MHB root, run:
 
@@ -276,7 +274,7 @@ The script converts **GWOSC, LEAVES, Iquique, Llaima, RadSeg, and RadioML** (3,4
 
 The script also generates `wav_data.jsonl` in the MHB root, containing **all 6,128 instances**. For the six converted tasks, it updates `data_type` and `input_ts.path`, `length`, `num_channel`, and `fs`; it additionally sets `input_ts.channel_detail` to `[]` for **Iquique, RadSeg, and RadioML**. The other seven tasks remain unchanged and retain their original WAV/FLAC input references where applicable.
 
-## Citation
+# Citation
 
 If you use MHB, please cite: