Certainly! Here’s a test case generator for the problem statement provided. This generator will create multiple datasets based on the given constraints and randomly decide the values or whether a sample is broken.

```python
import random

def generate_dataset():
    N = random.randint(2, 1000)
    # Generate the signal
    signal = []
    for i in range(1, N + 1):
        if random.random() < 0.2:  # 20% chance to be a broken sample
            signal.append('x')
        else:
            signal.append(random.randint(-10**9, 10**9))
    
    return N, signal

def tcgen():
    num_datasets = random.randint(1, 100)  # Number of datasets
    datasets = []

    for _ in range(num_datasets):
        N, signal = generate_dataset()
        datasets.append((N, signal))
    
    return datasets

# To generate and print a test case
if __name__ == "__main__":
    datasets = tcgen()
    for dataset in datasets:
        N, signal = dataset
        print(N)
        print(" ".join(map(str, signal)))
    print(0)  # End of input
```

### Explanation

1. **Function `generate_dataset`**:
    - Generates a single dataset.
    - Randomly decides the number of samples \( N \) within the range [2, 1000].
    - Constructs the audio signal with a 20% chance of each sample being broken (represented as 'x').
    - Otherwise, assigns a random integer value between \(-10^9\) and \(10^9\).

2. **Function `tcgen`**:
    - Randomly decides the number of datasets within the range [1, 100].
    - Calls `generate_dataset` to create each dataset.
    - Collects all datasets and returns them.

3. **Main Execution**:
    - Generates the datasets.
    - Prints each dataset in the required format.
    - Ends the input with a single `0`.

This generator ensures that all constraints are respected and provides a variety of test cases for robust testing.