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README.md

@@ -40,33 +40,33 @@ This repository provides the **Machine Hearing Benchmark (MHB)** introduced in [
 
 Multimodal LLMs have made substantial progress in perceiving the physical world, but their capabilities remain largely centered on signals that humans can directly see or hear. The physical world contains waves, vibrations, and oscillations far beyond human auditory perception. MHB investigates whether Audio Large Language Models (ALLMs) can generalize to this broader form of *machine hearing*.
 
-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.
+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 an input, a task-specific textual prompt, and a predefined label set.
 
 # 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.
+MHB integrates data from multiple sources. The following table lists the source or processed-release licenses and whether the corresponding input data are included in this repository.
 
 | MHB Task | Data Source | Source / Release License | Input Files | Reference |
 | --- | --- | :---: | :---: | :---: |
-| GWOSC | GWOSC_GW_Event | CC BY-NC-SA 4.0 (SciTS) | Included | [SciTS](https://huggingface.co/datasets/OpenTSLab/SciTS) |
-| LEAVES | LEAVES | CC BY 4.0 | Included | [NADC](https://nadc.china-vo.org/res/r100962/?lang=en) |
-| Iquique | Iquique | Not specified | **Reconstruction required** | [SeisBench](https://seisbench.readthedocs.io/en/main/pages/data/benchmark_datasets.html#iquique) |
-| Llaima | Llaima | CC BY-NC 3.0 | Included | [Mendeley Data](https://data.mendeley.com/datasets/dv8nwdd36k/1) |
-| Yaseen | Yaseen | Not specified | **Reconstruction required** | [Official GitHub](https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-) |
-| CirCor_loc | CirCor | ODC-BY 1.0 | Included | [PhysioNet](https://physionet.org/content/circor-heart-sound/1.0.3/) |
-| RadSeg | RadSeg | MIT | Included | [RadSeg](https://github.com/abcxyzi/radseg) |
-| RadioML | RadioML2016.10A | CC BY-NC-SA 4.0 | Included | [DeepSig](https://www.deepsig.ai/datasets/) |
-| ShipsEar | ShipsEar | Not specified | **Reconstruction required** | [Official database](https://underwaternoise.atlanttic.uvigo.es/) |
-| DeepShip | DeepShip | Not specified | **Reconstruction required** | [Official GitHub](https://github.com/irfankamboh/DeepShip) |
-| MarmAudio | MarmAudio | CC BY 4.0 | Included | [Zenodo](https://zenodo.org/records/15017207) |
+| GWOSC | GWOSC_GW_Event | <div align="center">CC BY-NC-SA 4.0 (SciTS)</div> | <div align="center">Included</div> | <div align="center"><a href="https://huggingface.co/datasets/OpenTSLab/SciTS">SciTS</a></div> |
+| LEAVES | LEAVES | <div align="center">CC BY 4.0</div> | <div align="center">Included</div> | <div align="center"><a href="https://nadc.china-vo.org/res/r100962/?lang=en">NADC</a></div> |
+| Iquique | Iquique | <div align="center">Not specified</div> | <div align="center"><strong>Reconstruction required</strong></div> | <div align="center"><a href="https://seisbench.readthedocs.io/en/main/pages/data/benchmark_datasets.html#iquique">SeisBench</a></div> |
+| Llaima | Llaima | <div align="center">CC BY-NC 3.0</div> | <div align="center">Included</div> | <div align="center"><a href="https://data.mendeley.com/datasets/dv8nwdd36k/1">Mendeley Data</a></div> |
+| Yaseen | Yaseen | <div align="center">Not specified</div> | <div align="center"><strong>Reconstruction required</strong></div> | <div align="center"><a href="https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-">Official GitHub</a></div> |
+| CirCor_loc | CirCor | <div align="center">ODC-BY 1.0</div> | <div align="center">Included</div> | <div align="center"><a href="https://physionet.org/content/circor-heart-sound/1.0.3/">PhysioNet</a></div> |
+| RadSeg | RadSeg | <div align="center">MIT</div> | <div align="center">Included</div> | <div align="center"><a href="https://github.com/abcxyzi/radseg">RadSeg</a></div> |
+| RadioML | RadioML2016.10A | <div align="center">CC BY-NC-SA 4.0</div> | <div align="center">Included</div> | <div align="center"><a href="https://www.deepsig.ai/datasets/">DeepSig</a></div> |
+| ShipsEar | ShipsEar | <div align="center">Not specified</div> | <div align="center"><strong>Reconstruction required</strong></div> | <div align="center"><a href="https://underwaternoise.atlanttic.uvigo.es/">Official database</a></div> |
+| DeepShip | DeepShip | <div align="center">Not specified</div> | <div align="center"><strong>Reconstruction required</strong></div> | <div align="center"><a href="https://github.com/irfankamboh/DeepShip">Official GitHub</a></div> |
+| MarmAudio | MarmAudio | <div align="center">CC BY 4.0</div> | <div align="center">Included</div> | <div align="center"><a href="https://zenodo.org/records/15017207">Zenodo</a></div> |
 | SDUST_bear | `sdust_bearing` via RMIS | <div align="center">MIT<br>(RMIS processed release)</div> | <div align="center">Included</div> | <div align="center"><a href="https://huggingface.co/datasets/jiangab/RMIS">RMIS</a></div> |
-| PU_cur | `pu_cur` via RMIS | MIT<br>(RMIS processed release) | Included | [RMIS](https://huggingface.co/datasets/jiangab/RMIS) |
+| PU_cur | `pu_cur` via RMIS | <div align="center">MIT<br>(RMIS processed release)</div> | <div align="center">Included</div> | <div align="center"><a href="https://huggingface.co/datasets/jiangab/RMIS">RMIS</a></div> |
 
 **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
 
-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/`.
+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 data stored under `raw_input/`.
 
 ```text
 ├── README.md
@@ -91,7 +91,7 @@ The repository contains a unified `meta_data.jsonl` file, task-specific data fol
 ```
 
 - **`meta_data.jsonl`**: The complete set of 6,128 instances and annotations, including entries for the four datasets that must be obtained externally.
-- **`<Dataset_Folder>/raw_input/`**: Signal files in their MHB original formats (`csv`, `npy`, `wav`, or `flac`). Directly hosted tasks contain their files; the four externally sourced tasks are reconstructed locally.
+- **`<Dataset_Folder>/raw_input/`**: Input files in their original formats (`csv`, `npy`, `wav`, or `flac`). Directly hosted tasks contain their files; the four externally sourced tasks are reconstructed locally.
 - **`process/map_*.py`**: Scripts for selecting and mapping externally obtained data to the exact names and relative paths in `meta_data.jsonl`.
 - **`process/process_raw2wav.py`**: Optional conversion of six non-WAV tasks to `wav_input/`, also generating `wav_data.jsonl`.
 
@@ -153,10 +153,10 @@ Each line is a JSON object representing one instance. The following is a shorten
 | `id` | Unique identifier of the instance. |
 | `data_type` | Input file format: `csv`, `npy`, `wav`, or `flac`. |
 | `input_ts.path` | Path to the input file, **relative to the MHB repository root**. For externally sourced datasets, the corresponding mapping script restores this file locally. |
-| `input_ts.length` | Number of time steps in the original signal; multi-channel signals have additional elements across channels. |
-| `input_ts.num_channel` | Number of signal channels. |
+| `input_ts.length` | Input sequence length in time steps. |
+| `input_ts.num_channel` | Number of input channels. |
 | `input_ts.channel_detail` | Optional channel descriptions; empty when not provided. |
-| `input_ts.fs` | Original signal sampling frequency in Hz. |
+| `input_ts.fs` | Original sampling rate in Hz. |
 | `input_ts.timestamps` | Optional timestamp information, usually an empty list. |
 | `input_text` | Task-specific textual instruction given to the model. |
 | `gt_text` | Reference answer in text form. |
@@ -164,7 +164,7 @@ Each line is a JSON object representing one instance. The following is a shorten
 | `gt_result.gt_class` | Ground-truth class label as a list. |
 | `meta_data` | Source-specific metadata (e.g., `trace_name` for Iquique or `ori_file_path` and `seg_index` for ShipsEar); fields vary by task. |
 
-Paths in `meta_data.jsonl` are not absolute paths. For example, load a signal by joining the repository root with `record["input_ts"]["path"]`.
+Paths in `meta_data.jsonl` are relative to the repository root. For example, input data can be loaded using `record["input_ts"]["path"]` relative to the repository root.
 
 For models that generate additional explanations or text, users may append an output-format constraint to the end of `input_text` during inference, such as:
 
@@ -174,7 +174,7 @@ For models that generate additional explanations or text, users may append an ou
 
 # 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.
+The input data for **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.
 
 For the MHB mapping scripts, install the necessary Python packages: