def extract_fragment_features_librosa(time_series, start_time, end_time, description, PID, EXP):
    """
    Extracts / aggregates features in a certain time period within the time series data

    Parameters
    ----------
    time_series: pandas.core.frame.DataFrame
        A pandas data.frame for which there is a column 'timestamps'
        with numeric timestamps.
    start_time: the start_time of the data to include.
    end_time: the end_time of the data to include.
    description: label to add as extra column.
    PID: participant id to add as extra column.
    EXP: experiment id to add as extra column.

    Returns
    -------
    agg_col: pandas.core.frame.DataFrame
        Single-row DataFrame containing the aggregated values.
    """
    filter_timerange = (time_series['timestamp'] >= start_time) & (time_series['timestamp'] < end_time)
    selected_features = time_series[filter_timerange]

    selected_features = selected_features.rename(columns={'zrc': 'zcrate'})
    selected_features = selected_features[['pitch','rmse','zcrate']]
    aggregated_features = selected_features.aggregate(['mean'])
    
    aggregated_features['participant_id'] = PID
    aggregated_features['experiment_id'] = EXP
    aggregated_features['start_time'] = start_time
    aggregated_features['end_time'] = end_time
    aggregated_features['description'] = description
    return aggregated_features