diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py
index a0a4d0823c..0c3b9f95d1 100644
--- a/src/spikeinterface/sorters/internal/spyking_circus2.py
+++ b/src/spikeinterface/sorters/internal/spyking_circus2.py
@@ -6,7 +6,7 @@
 
 from spikeinterface.core import NumpySorting, load_extractor, BaseRecording, get_noise_levels, extract_waveforms
 from spikeinterface.core.job_tools import fix_job_kwargs
-from spikeinterface.preprocessing import bandpass_filter, common_reference, zscore
+from spikeinterface.preprocessing import common_reference, zscore, whiten, highpass_filter
 
 try:
     import hdbscan
@@ -22,7 +22,7 @@ class Spykingcircus2Sorter(ComponentsBasedSorter):
     _default_params = {
         "general": {"ms_before": 2, "ms_after": 2, "radius_um": 100},
         "waveforms": {"max_spikes_per_unit": 200, "overwrite": True, "sparse": True, "method": "ptp", "threshold": 1},
-        "filtering": {"dtype": "float32"},
+        "filtering": {"freq_min": 150, "dtype": "float32"},
         "detection": {"peak_sign": "neg", "detect_threshold": 5},
         "selection": {"n_peaks_per_channel": 5000, "min_n_peaks": 20000},
         "localization": {},
@@ -60,11 +60,12 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         ## First, we are filtering the data
         filtering_params = params["filtering"].copy()
         if params["apply_preprocessing"]:
-            recording_f = bandpass_filter(recording, **filtering_params)
+            recording_f = highpass_filter(recording, **filtering_params)
             recording_f = common_reference(recording_f)
         else:
             recording_f = recording
 
+        # recording_f = whiten(recording_f, dtype="float32")
         recording_f = zscore(recording_f, dtype="float32")
 
         ## Then, we are detecting peaks with a locally_exclusive method
@@ -98,10 +99,10 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         ## We launch a clustering (using hdbscan) relying on positions and features extracted on
         ## the fly from the snippets
         clustering_params = params["clustering"].copy()
-        clustering_params["waveforms_kwargs"] = params["waveforms"]
+        clustering_params["waveforms"] = params["waveforms"].copy()
 
         for k in ["ms_before", "ms_after"]:
-            clustering_params["waveforms_kwargs"][k] = params["general"][k]
+            clustering_params["waveforms"][k] = params["general"][k]
 
         clustering_params.update(dict(shared_memory=params["shared_memory"]))
         clustering_params["job_kwargs"] = job_kwargs
diff --git a/src/spikeinterface/sortingcomponents/clustering/clustering_tools.py b/src/spikeinterface/sortingcomponents/clustering/clustering_tools.py
index 891c355448..b4938717f8 100644
--- a/src/spikeinterface/sortingcomponents/clustering/clustering_tools.py
+++ b/src/spikeinterface/sortingcomponents/clustering/clustering_tools.py
@@ -593,29 +593,34 @@ def remove_duplicates_via_matching(
 
     chunk_size = duration + 3 * margin
 
-    method_kwargs.update(
+    local_params = method_kwargs.copy()
+
+    local_params.update(
         {
             "waveform_extractor": waveform_extractor,
             "noise_levels": noise_levels,
             "amplitudes": [0.95, 1.05],
-            "omp_min_sps": 0.1,
+            "omp_min_sps": 0.05,
         }
     )
 
+    spikes_per_units, counts = np.unique(waveform_extractor.sorting.to_spike_vector()["unit_index"], return_counts=True)
+    indices = np.argsort(counts)
+
     ignore_ids = []
     similar_templates = [[], []]
 
-    for i in range(nb_templates):
+    for i in indices:
         t_start = padding + i * duration
         t_stop = padding + (i + 1) * duration
 
         sub_recording = recording.frame_slice(t_start - half_marging, t_stop + half_marging)
-        method_kwargs.update({"ignored_ids": ignore_ids + [i]})
+        local_params.update({"ignored_ids": ignore_ids + [i]})
         spikes, computed = find_spikes_from_templates(
-            sub_recording, method=method, method_kwargs=method_kwargs, extra_outputs=True, **job_kwargs
+            sub_recording, method=method, method_kwargs=local_params, extra_outputs=True, **job_kwargs
         )
         if method == "circus-omp-svd":
-            method_kwargs.update(
+            local_params.update(
                 {
                     "overlaps": computed["overlaps"],
                     "templates": computed["templates"],
@@ -629,7 +634,7 @@ def remove_duplicates_via_matching(
                 }
             )
         elif method == "circus-omp":
-            method_kwargs.update(
+            local_params.update(
                 {
                     "overlaps": computed["overlaps"],
                     "templates": computed["templates"],
@@ -661,7 +666,7 @@ def remove_duplicates_via_matching(
     labels = np.unique(new_labels)
     labels = labels[labels >= 0]
 
-    del recording, sub_recording, method_kwargs
+    del recording, sub_recording, local_params, waveform_extractor
     os.remove(tmp_filename)
 
     return labels, new_labels
diff --git a/src/spikeinterface/sortingcomponents/clustering/random_projections.py b/src/spikeinterface/sortingcomponents/clustering/random_projections.py
index 1f97bf5201..a81458d7a8 100644
--- a/src/spikeinterface/sortingcomponents/clustering/random_projections.py
+++ b/src/spikeinterface/sortingcomponents/clustering/random_projections.py
@@ -199,9 +199,8 @@ def sigmoid(x, L, x0, k, b):
             recording,
             sorting,
             waveform_folder,
-            ms_before=params["ms_before"],
-            ms_after=params["ms_after"],
             **params["job_kwargs"],
+            **params["waveforms"],
             return_scaled=False,
             mode=mode,
         )