diff --git a/batdetect2/utils/detector_utils.py b/batdetect2/utils/detector_utils.py
index 74193b3..8d6ca7f 100644
--- a/batdetect2/utils/detector_utils.py
+++ b/batdetect2/utils/detector_utils.py
@@ -2,6 +2,7 @@ import json
 import os
 from typing import Any, Iterator, List, Optional, Tuple, Union
 
+import librosa
 import numpy as np
 import pandas as pd
 import torch
@@ -66,7 +67,6 @@ def list_audio_files(ip_dir: str) -> List[str]:
 
     Raises:
         FileNotFoundError: Input directory not found.
-
     """
     matches = []
     for root, _, filenames in os.walk(ip_dir):
@@ -269,6 +269,7 @@ def convert_results(
     spec_feats,
     cnn_feats,
     spec_slices,
+    nyquist_freq: Optional[float] = None,
 ) -> RunResults:
     """Convert results to dictionary as expected by the annotation tool.
 
@@ -284,8 +285,8 @@ def convert_results(
 
     Returns:
         dict: Dictionary with results.
-
     """
+
     pred_dict = format_single_result(
         file_id,
         time_exp,
@@ -294,6 +295,14 @@ def convert_results(
         params["class_names"],
     )
 
+    # Remove high frequency detections
+    if nyquist_freq is not None:
+        pred_dict["annotation"] = [
+            pred
+            for pred in pred_dict["annotation"]
+            if pred["high_freq"] <= nyquist_freq
+        ]
+
     # combine into final results dictionary
     results: RunResults = {
         "pred_dict": pred_dict,
@@ -326,7 +335,6 @@ def save_results_to_file(results, op_path: str) -> None:
     Args:
         results (dict): Results.
         op_path (str): Output path.
-
     """
     # make directory if it does not exist
     if not os.path.isdir(os.path.dirname(op_path)):
@@ -488,7 +496,6 @@ def iterate_over_chunks(
     chunk_start : float
         Start time of chunk in seconds.
     chunk : np.ndarray
-
     """
     nsamples = audio.shape[0]
     duration_full = nsamples / samplerate
@@ -694,7 +701,6 @@ def process_audio_array(
         The array is of shape (num_detections, num_features).
     spec : torch.Tensor
         Spectrogram of the audio used as input.
-
     """
     pred_nms, features, spec = _process_audio_array(
         audio,
@@ -746,6 +752,10 @@ def process_file(
     cnn_feats = []
     spec_slices = []
 
+    # Get original sampling rate
+    file_samp_rate = librosa.get_samplerate(audio_file)
+    orig_samp_rate = file_samp_rate * config.get("time_expansion", 1) or 1
+
     # load audio file
     sampling_rate, audio_full = au.load_audio(
         audio_file,
@@ -793,9 +803,7 @@ def process_file(
 
         if config["spec_slices"]:
             # FIX: This is not currently working. Returns empty slices
-            spec_slices.extend(
-                feats.extract_spec_slices(spec_np, pred_nms)
-            )
+            spec_slices.extend(feats.extract_spec_slices(spec_np, pred_nms))
 
     # Merge results from chunks
     predictions, spec_feats, cnn_feats, spec_slices = _merge_results(
@@ -815,6 +823,7 @@ def process_file(
         spec_feats=spec_feats,
         cnn_feats=cnn_feats,
         spec_slices=spec_slices,
+        nyquist_freq=orig_samp_rate / 2,
     )
 
     # summarize results