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README.md

@@ -33,14 +33,14 @@ pretty_name: 'Machine Hearing Benchmark (MHB)'
 # Machine Hearing Benchmark (MHB)
 
 <p align="center">
-  <img src="process/MHB.png" alt="Overview of MHB across seven domains and 13 tasks" width="600">
+  <img src="process/MHB.png" alt="Overview of MHB across seven domains and 13 tasks" width="800">
 </p>
 
 This repository provides the **Machine Hearing Benchmark (MHB)** introduced in [**Machine Hearing: Can Audio LLMs Generalize Beyond Human Auditory Perception?**](https://arxiv.org/abs/2611.10693)
 
 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 a signal, a task-specific textual instruction, and a predefined label set.
 
 ## Dataset Licenses and Distribution
 
@@ -90,7 +90,7 @@ The repository contains a unified `meta_data.jsonl` file, task-specific data fol
 # wav_data.jsonl is generated at the repository root.
 ```
 
-- **`meta_data.jsonl`**: The complete set of 6,128 test instances and annotations, including entries for the four datasets that must be obtained externally.
+- **`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.
 - **`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`.
@@ -120,7 +120,7 @@ All 13 tasks in MHB are formulated as classification tasks, with detailed task d
 
 ## `meta_data.jsonl` Format
 
-Each line is a JSON object representing one test instance. The following is a shortened example; the text fields are illustrative:
+Each line is a JSON object representing one instance. The following is a shortened example; the text fields are illustrative:
 
 ```json
 {