def _extract_features(self, preprocessed_data, verbose=True):
        """
        Parameters
        ----------
        preprocessed_data : SArray

        Returns
        -------
        numpy array containing the deep features
        """
        last_progress_update = _time.time()
        progress_header_printed = False

        deep_features = _tc.SArrayBuilder(_np.ndarray)
        from mxnet.gluon import utils

        if _mac_ver() < (10, 14):
            # Use MXNet
            preprocessed_data = mx.nd.array(preprocessed_data)

            ctx_list = self.ctx
            if len(preprocessed_data) < len(ctx_list):
                ctx_list = ctx_list[:len(preprocessed_data)]
            batches = utils.split_and_load(preprocessed_data, ctx_list=ctx_list, even_split=False)

            for i, cur_batch in enumerate(batches):
                y = self.vggish_model.forward(cur_batch).asnumpy()
                for j in y:
                    deep_features.append(j)

                # If `verbose` is set, print an progress update about every 20s
                if verbose and _time.time() - last_progress_update >= 20:
                    if not progress_header_printed:
                        print("Extracting deep features -")
                        progress_header_printed = True
                    print("Extracted {} of {} batches".format(i, len(batches)))
                    last_progress_update = _time.time()
            if progress_header_printed:
                print("Extracted {} of {} batches\n".format(len(batches), len(batches)))

        else:
            # Use Core ML
            for i, cur_example in enumerate(preprocessed_data):
                for cur_frame in cur_example:
                    x = {'input1': [cur_frame]}
                    y = self.vggish_model.predict(x)
                    deep_features.append(y['output1'])

                # If `verbose` is set, print an progress update about every 20s
                if verbose and _time.time() - last_progress_update >= 20:
                    if not progress_header_printed:
                        print("Extracting deep features -")
                        progress_header_printed = True
                    print("Extracted {} of {}".format(i, len(preprocessed_data)))
                    last_progress_update = _time.time()
            if progress_header_printed:
                print("Extracted {} of {}\n".format(len(preprocessed_data), len(preprocessed_data)))

        return deep_features.close()
Parameters
        ----------
        preprocessed_data : SArray

        Returns
        -------
        numpy array containing the deep features